---
title: "eDesk - CECIIS"
output:
flexdashboard::flex_dashboard:
social: menu
orientation: columns
vertical_layout: fill
source_code: embed
---
```{css, echo=FALSE}
.sidebar { overflow: auto; }
.dataTables_scrollBody {
height:95% !important;
max-height:95% !important;
}
.chart-stage-flex {
overflow:auto !important;
}
```
```{r setup, include=FALSE}
library(readxl)
library(tidyverse)
library(car)
library(lsr)
library(kableExtra)
library(FSA)
podaci <- read_excel('Podaci.xlsx')
imena <- names(podaci)
podaci <- podaci %>% rename_with(.fn = ~paste0("Q", substring(.,1,regexpr("\\.", .) - 1)), .cols = 9:length(imena))
podaci$Country <- factor(podaci$Country)
podaci9_18 <- podaci %>% select(Country, 9:18) %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
podaci19_28 <- podaci %>% select(Country, 19:28) %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
podaci29_38 <- podaci %>% select(Country, 29:38) %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
podaci39_48 <- podaci %>% select(Country, 39:48) %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
podaci49_58 <- podaci %>% select(Country, 49:58) %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
podaci49_58$Answer = factor(podaci49_58$Answer)
levels(podaci49_58$Answer) <- c(levels(podaci49_58$Answer),4,5)
podaci59_69 <- podaci %>% select(Country, 59:69) %>% drop_na() %>%
pivot_longer(!Country, names_to = "Question", values_to = "Answer")
ppodaci9_18 <- podaci %>% select("Study field", 9:18) %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
ppodaci19_28 <- podaci %>% select("Study field", 19:28) %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
ppodaci29_38 <- podaci %>% select("Study field", 29:38) %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
ppodaci39_48 <- podaci %>% select("Study field", 39:48) %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
ppodaci49_58 <- podaci %>% select("Study field", 49:58) %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
ppodaci49_58$Answer = factor(ppodaci49_58$Answer)
levels(ppodaci49_58$Answer) <- c(levels(ppodaci49_58$Answer),4,5)
ppodaci59_69 <- podaci %>% select("Study field", 59:69) %>% drop_na() %>%
pivot_longer(!`Study field`, names_to = "Question", values_to = "Answer")
swr = function(string, nwrap=10) {
paste(strwrap(string, width=nwrap), collapse="\n")
}
swr = Vectorize(swr)
ppodaci9_18$`Study field` = swr(ppodaci9_18$`Study field`)
ppodaci19_28$`Study field` = swr(ppodaci19_28$`Study field`)
ppodaci29_38$`Study field` = swr(ppodaci29_38$`Study field`)
ppodaci39_48$`Study field` = swr(ppodaci39_48$`Study field`)
ppodaci49_58$`Study field` = swr(ppodaci49_58$`Study field`)
ppodaci59_69$`Study field` = swr(ppodaci59_69$`Study field`)
Q1 <- aov(podaci$Q1 ~ podaci$Country)
Q2 <- aov(podaci$Q2 ~ podaci$Country)
Q3 <- aov(podaci$Q3 ~ podaci$Country)
Q4 <- aov(podaci$Q4 ~ podaci$Country)
Q5 <- aov(podaci$Q5 ~ podaci$Country)
Q6 <- aov(podaci$Q6 ~ podaci$Country)
Q7 <- aov(podaci$Q7 ~ podaci$Country)
Q8 <- aov(podaci$Q8 ~ podaci$Country)
Q9 <- aov(podaci$Q9 ~ podaci$Country)
Q10 <- aov(podaci$Q10 ~ podaci$Country)
Q11 <- aov(podaci$Q11 ~ podaci$Country)
Q12 <- aov(podaci$Q12 ~ podaci$Country)
Q13 <- aov(podaci$Q13 ~ podaci$Country)
Q14 <- aov(podaci$Q14 ~ podaci$Country)
Q15 <- aov(podaci$Q15 ~ podaci$Country)
Q16 <- aov(podaci$Q16 ~ podaci$Country)
Q17 <- aov(podaci$Q17 ~ podaci$Country)
Q18 <- aov(podaci$Q18 ~ podaci$Country)
Q19 <- aov(podaci$Q19 ~ podaci$Country)
Q20 <- aov(podaci$Q20 ~ podaci$Country)
Q21 <- aov(podaci$Q21 ~ podaci$Country)
Q22 <- aov(podaci$Q22 ~ podaci$Country)
Q23 <- aov(podaci$Q23 ~ podaci$Country)
Q24 <- aov(podaci$Q24 ~ podaci$Country)
Q26 <- aov(podaci$Q26 ~ podaci$Country)
Q27 <- aov(podaci$Q27 ~ podaci$Country)
Q28 <- aov(podaci$Q28 ~ podaci$Country)
Q29 <- aov(podaci$Q29 ~ podaci$Country)
Q30 <- aov(podaci$Q30 ~ podaci$Country)
Q31 <- aov(podaci$Q31 ~ podaci$Country)
Q32 <- aov(podaci$Q32 ~ podaci$Country)
Q33 <- aov(podaci$Q33 ~ podaci$Country)
Q35 <- aov(podaci$Q35 ~ podaci$Country)
Q36 <- aov(podaci$Q36 ~ podaci$Country)
Q37 <- aov(podaci$Q37 ~ podaci$Country)
Q38 <- aov(podaci$Q38 ~ podaci$Country)
Q39 <- aov(podaci$Q39 ~ podaci$Country)
Q40 <- aov(podaci$Q40 ~ podaci$Country)
Q41 <- aov(podaci$Q41 ~ podaci$Country)
Q42 <- aov(podaci$Q42 ~ podaci$Country)
Q43 <- aov(podaci$Q43 ~ podaci$Country)
Q44 <- aov(podaci$Q44 ~ podaci$Country)
Q45 <- aov(podaci$Q45 ~ podaci$Country)
Q46 <- aov(podaci$Q46 ~ podaci$Country)
Q47 <- aov(podaci$Q47 ~ podaci$Country)
Q48 <- aov(podaci$Q48 ~ podaci$Country)
Q49 <- aov(podaci$Q49 ~ podaci$Country)
Q50 <- aov(podaci$Q50 ~ podaci$Country)
Q51 <- aov(podaci$Q51 ~ podaci$Country)
Q52 <- aov(podaci$Q52 ~ podaci$Country)
Q53 <- aov(podaci$Q53 ~ podaci$Country)
Q58 <- aov(podaci$Q58 ~ podaci$Country)
Q59 <- aov(podaci$Q59 ~ podaci$Country)
Q60 <- aov(podaci$Q60 ~ podaci$Country)
Q61 <- aov(podaci$Q61 ~ podaci$Country)
Q62 <- aov(podaci$Q62 ~ podaci$Country)
Q63 <- aov(podaci$Q63 ~ podaci$Country)
Q64 <- aov(podaci$Q64 ~ podaci$Country)
Q65 <- aov(podaci$Q65 ~ podaci$Country)
Q66 <- aov(podaci$Q66 ~ podaci$Country)
Q67 <- aov(podaci$Q67 ~ podaci$Country)
PQ1 <- aov(podaci$Q1 ~ podaci$`Study field`)
PQ2 <- aov(podaci$Q2 ~ podaci$`Study field`)
PQ3 <- aov(podaci$Q3 ~ podaci$`Study field`)
PQ4 <- aov(podaci$Q4 ~ podaci$`Study field`)
PQ5 <- aov(podaci$Q5 ~ podaci$`Study field`)
PQ6 <- aov(podaci$Q6 ~ podaci$`Study field`)
PQ7 <- aov(podaci$Q7 ~ podaci$`Study field`)
PQ8 <- aov(podaci$Q8 ~ podaci$`Study field`)
PQ9 <- aov(podaci$Q9 ~ podaci$`Study field`)
PQ10 <- aov(podaci$Q10 ~ podaci$`Study field`)
PQ11 <- aov(podaci$Q11 ~ podaci$`Study field`)
PQ12 <- aov(podaci$Q12 ~ podaci$`Study field`)
PQ13 <- aov(podaci$Q13 ~ podaci$`Study field`)
PQ14 <- aov(podaci$Q14 ~ podaci$`Study field`)
PQ15 <- aov(podaci$Q15 ~ podaci$`Study field`)
PQ16 <- aov(podaci$Q16 ~ podaci$`Study field`)
PQ17 <- aov(podaci$Q17 ~ podaci$`Study field`)
PQ18 <- aov(podaci$Q18 ~ podaci$`Study field`)
PQ19 <- aov(podaci$Q19 ~ podaci$`Study field`)
PQ20 <- aov(podaci$Q20 ~ podaci$`Study field`)
PQ21 <- aov(podaci$Q21 ~ podaci$`Study field`)
PQ22 <- aov(podaci$Q22 ~ podaci$`Study field`)
PQ23 <- aov(podaci$Q23 ~ podaci$`Study field`)
PQ24 <- aov(podaci$Q24 ~ podaci$`Study field`)
PQ26 <- aov(podaci$Q26 ~ podaci$`Study field`)
PQ27 <- aov(podaci$Q27 ~ podaci$`Study field`)
PQ28 <- aov(podaci$Q28 ~ podaci$`Study field`)
PQ29 <- aov(podaci$Q29 ~ podaci$`Study field`)
PQ30 <- aov(podaci$Q30 ~ podaci$`Study field`)
PQ31 <- aov(podaci$Q31 ~ podaci$`Study field`)
PQ32 <- aov(podaci$Q32 ~ podaci$`Study field`)
PQ33 <- aov(podaci$Q33 ~ podaci$`Study field`)
PQ35 <- aov(podaci$Q35 ~ podaci$`Study field`)
PQ36 <- aov(podaci$Q36 ~ podaci$`Study field`)
PQ37 <- aov(podaci$Q37 ~ podaci$`Study field`)
PQ38 <- aov(podaci$Q38 ~ podaci$`Study field`)
PQ39 <- aov(podaci$Q39 ~ podaci$`Study field`)
PQ40 <- aov(podaci$Q40 ~ podaci$`Study field`)
PQ41 <- aov(podaci$Q41 ~ podaci$`Study field`)
PQ42 <- aov(podaci$Q42 ~ podaci$`Study field`)
PQ43 <- aov(podaci$Q43 ~ podaci$`Study field`)
PQ44 <- aov(podaci$Q44 ~ podaci$`Study field`)
PQ45 <- aov(podaci$Q45 ~ podaci$`Study field`)
PQ46 <- aov(podaci$Q46 ~ podaci$`Study field`)
PQ47 <- aov(podaci$Q47 ~ podaci$`Study field`)
PQ48 <- aov(podaci$Q48 ~ podaci$`Study field`)
PQ49 <- aov(podaci$Q49 ~ podaci$`Study field`)
PQ50 <- aov(podaci$Q50 ~ podaci$`Study field`)
PQ51 <- aov(podaci$Q51 ~ podaci$`Study field`)
PQ52 <- aov(podaci$Q52 ~ podaci$`Study field`)
PQ53 <- aov(podaci$Q53 ~ podaci$`Study field`)
PQ58 <- aov(podaci$Q58 ~ podaci$`Study field`)
PQ59 <- aov(podaci$Q59 ~ podaci$`Study field`)
PQ60 <- aov(podaci$Q60 ~ podaci$`Study field`)
PQ61 <- aov(podaci$Q61 ~ podaci$`Study field`)
PQ62 <- aov(podaci$Q62 ~ podaci$`Study field`)
PQ63 <- aov(podaci$Q63 ~ podaci$`Study field`)
PQ64 <- aov(podaci$Q64 ~ podaci$`Study field`)
PQ65 <- aov(podaci$Q65 ~ podaci$`Study field`)
PQ66 <- aov(podaci$Q66 ~ podaci$`Study field`)
PQ67 <- aov(podaci$Q67 ~ podaci$`Study field`)
```
Pitanja: 1 - 10 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (1 - 10)
```{r fig.width=10}
ggplot(podaci9_18, aes(x=factor(Answer), fill=Country, color=Country)) +
geom_bar(alpha=.5) +
facet_grid(Country ~ factor(Question,levels=c("Q1","Q2","Q3","Q4","Q5","Q6","Q7","Q8","Q9","Q10"))) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50)
```
Pitanja: 11 - 20 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (11 - 20)
```{r fig.width=10}
ggplot(podaci19_28, aes(x=factor(Answer), fill=Country, color=Country)) +
geom_bar(alpha=.5) + facet_grid(Country ~ factor(Question)) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50)
```
Pitanja: 21 - 30 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (21 - 30)
```{r fig.width=10}
ggplot(podaci29_38, aes(x=factor(Answer), fill=Country, color=Country)) +
geom_bar(alpha=.5) + facet_grid(Country ~ factor(Question)) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50)
```
Pitanja: 31 - 40 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (31 - 40)
```{r fig.width=10}
ggplot(podaci39_48, aes(x=factor(Answer), fill=Country, color=Country)) +
geom_bar(alpha=.5) + facet_grid(Country ~ factor(Question)) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50)
```
Pitanja: 41 - 50 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (41 - 50)
```{r fig.width=10}
ggplot(podaci49_58, aes(x=Answer, fill=Country, color=Country)) +
geom_bar(alpha=.5) + facet_grid(Country ~ factor(Question)) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50) + scale_x_discrete(drop=FALSE)
```
Pitanja: 51 - 61 {data-navmenu="Pitanja vs države"}
=======================================================================
### pitanja (51 - 61)
```{r fig.width=11}
ggplot(podaci59_69, aes(x=factor(Answer), fill=Country, color=Country)) +
geom_bar(alpha=.5) + facet_grid(Country ~ factor(Question)) +
theme(legend.position="none") + xlab("Answer") + ylim(0,50)
```
Pitanja: 1 - 10 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (1 - 10)
```{r fig.width=10}
ggplot(ppodaci9_18, aes(x=factor(Answer), fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question,levels=c("Q1","Q2","Q3","Q4","Q5","Q6","Q7","Q8","Q9","Q10"))) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40)
```
Pitanja: 11 - 20 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (11 - 20)
```{r fig.width=10}
ggplot(ppodaci19_28, aes(x=factor(Answer), fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question)) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40)
```
Pitanja: 21 - 30 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (21 - 30)
```{r fig.width=10}
ggplot(ppodaci29_38, aes(x=factor(Answer), fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question)) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40)
```
Pitanja: 31 - 40 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (31 - 40)
```{r fig.width=10}
ggplot(ppodaci39_48, aes(x=factor(Answer), fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question)) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40)
```
Pitanja: 41 - 50 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (41 - 50)
```{r fig.width=10}
ggplot(ppodaci49_58, aes(x=Answer, fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question)) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40) + scale_x_discrete(drop=FALSE)
```
Pitanja: 51 - 61 {data-navmenu="Pitanja vs područje"}
=======================================================================
### pitanja (51 - 61)
```{r fig.width=10}
ggplot(ppodaci59_69, aes(x=factor(Answer), fill=`Study field`, color=`Study field`)) +
geom_bar(alpha=.5) +
facet_grid(factor(`Study field`) ~ factor(Question)) +
theme(legend.position="none", strip.text.y = element_text(size = 7)) + xlab("Answer") + ylim(0,40)
```
Q1 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q1** {data-height=200}
```{r, echo = F}
summary(Q1)
```
### ONEWAY-test rezultati: **Q1** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q1 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q1, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q1** {data-height=200}
```{r, echo = F}
kruskal.test(Q1 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q1)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q1 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q2 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q2** {data-height=200}
```{r, echo = F}
summary(Q2)
```
### ONEWAY-test rezultati: **Q2** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q2 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q2, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q2** {data-height=200}
```{r, echo = F}
kruskal.test(Q2 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q2)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q2 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q3 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q3** {data-height=200}
```{r, echo = F}
summary(Q3)
```
### ONEWAY-test rezultati: **Q3** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q3 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q3, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q3** {data-height=200}
```{r, echo = F}
kruskal.test(Q3 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q3)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q3 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q4 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q4** {data-height=200}
```{r, echo = F}
summary(Q4)
```
### ONEWAY-test rezultati: **Q4** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q4 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q4, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q4** {data-height=200}
```{r, echo = F}
kruskal.test(Q4 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q4)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q4 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q5 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q5** {data-height=200}
```{r, echo = F}
summary(Q5)
```
### ONEWAY-test rezultati: **Q5** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q5 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q5, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q5** {data-height=200}
```{r, echo = F}
kruskal.test(Q5 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q5)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q5 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q6 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q6** {data-height=200}
```{r, echo = F}
summary(Q6)
```
### ONEWAY-test rezultati: **Q6** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q6 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q6, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q6** {data-height=200}
```{r, echo = F}
kruskal.test(Q6 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q6)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q6 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q7 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q7** {data-height=200}
```{r, echo = F}
summary(Q7)
```
### ONEWAY-test rezultati: **Q7** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q7 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q7, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q7** {data-height=200}
```{r, echo = F}
kruskal.test(Q7 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q7)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q7 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q8 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q8** {data-height=200}
```{r, echo = F}
summary(Q8)
```
### ONEWAY-test rezultati: **Q8** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q8 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q8, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q8** {data-height=200}
```{r, echo = F}
kruskal.test(Q8 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q8)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q8 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q9 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q9** {data-height=200}
```{r, echo = F}
summary(Q9)
```
### ONEWAY-test rezultati: **Q9** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q9 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q9, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q9** {data-height=200}
```{r, echo = F}
kruskal.test(Q9 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q9)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q9 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q10 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q10** {data-height=200}
```{r, echo = F}
summary(Q10)
```
### ONEWAY-test rezultati: **Q10** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q10 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q10, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q10** {data-height=200}
```{r, echo = F}
kruskal.test(Q10 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q10)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q10 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q11 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q11** {data-height=200}
```{r, echo = F}
summary(Q11)
```
### ONEWAY-test rezultati: **Q11** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q11 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q11, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q11** {data-height=200}
```{r, echo = F}
kruskal.test(Q11 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q11)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q11 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q12 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q12** {data-height=200}
```{r, echo = F}
summary(Q12)
```
### ONEWAY-test rezultati: **Q12** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q12 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q12, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q12** {data-height=200}
```{r, echo = F}
kruskal.test(Q12 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q12)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q12 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q13 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q13** {data-height=200}
```{r, echo = F}
summary(Q13)
```
### ONEWAY-test rezultati: **Q13** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q13 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q13, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q13** {data-height=200}
```{r, echo = F}
kruskal.test(Q13 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q13)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q13 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q14 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q14** {data-height=200}
```{r, echo = F}
summary(Q14)
```
### ONEWAY-test rezultati: **Q14** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q14 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q14, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q14** {data-height=200}
```{r, echo = F}
kruskal.test(Q14 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q14)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q14 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q15 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q15** {data-height=200}
```{r, echo = F}
summary(Q15)
```
### ONEWAY-test rezultati: **Q15** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q15 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q15, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q15** {data-height=200}
```{r, echo = F}
kruskal.test(Q15 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q15)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q15 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q16 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q16** {data-height=200}
```{r, echo = F}
summary(Q16)
```
### ONEWAY-test rezultati: **Q16** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q16 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q16, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q16** {data-height=200}
```{r, echo = F}
kruskal.test(Q16 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q16)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q16 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q17 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q17** {data-height=200}
```{r, echo = F}
summary(Q17)
```
### ONEWAY-test rezultati: **Q17** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q17 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q17, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q17** {data-height=200}
```{r, echo = F}
kruskal.test(Q17 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q17)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q17 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q18 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q18** {data-height=200}
```{r, echo = F}
summary(Q18)
```
### ONEWAY-test rezultati: **Q18** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q18 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q18, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q18** {data-height=200}
```{r, echo = F}
kruskal.test(Q18 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q18)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q18 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q19 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q19** {data-height=200}
```{r, echo = F}
summary(Q19)
```
### ONEWAY-test rezultati: **Q19** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q19 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q19, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q19** {data-height=200}
```{r, echo = F}
kruskal.test(Q19 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q19)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q19 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q20 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q20** {data-height=200}
```{r, echo = F}
summary(Q20)
```
### ONEWAY-test rezultati: **Q20** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q20 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q20, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q20** {data-height=200}
```{r, echo = F}
kruskal.test(Q20 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q20)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q20 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q21 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q21** {data-height=200}
```{r, echo = F}
summary(Q21)
```
### ONEWAY-test rezultati: **Q21** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q21 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q21, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q21** {data-height=200}
```{r, echo = F}
kruskal.test(Q21 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q21)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q21 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q22 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q22** {data-height=200}
```{r, echo = F}
summary(Q22)
```
### ONEWAY-test rezultati: **Q22** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q22 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q22, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q22** {data-height=200}
```{r, echo = F}
kruskal.test(Q22 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q22)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q22 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q23 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q23** {data-height=200}
```{r, echo = F}
summary(Q23)
```
### ONEWAY-test rezultati: **Q23** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q23 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q23, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q23** {data-height=200}
```{r, echo = F}
kruskal.test(Q23 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q23)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q23 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q24 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q24** {data-height=200}
```{r, echo = F}
summary(Q24)
```
### ONEWAY-test rezultati: **Q24** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q24 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q24, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q24** {data-height=200}
```{r, echo = F}
kruskal.test(Q24 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q24)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q24 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q26 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q26** {data-height=200}
```{r, echo = F}
summary(Q26)
```
### ONEWAY-test rezultati: **Q26** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q26 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q26, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q26** {data-height=200}
```{r, echo = F}
kruskal.test(Q26 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q26)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q26 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q27 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q27** {data-height=200}
```{r, echo = F}
summary(Q27)
```
### ONEWAY-test rezultati: **Q27** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q27 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q27, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q27** {data-height=200}
```{r, echo = F}
kruskal.test(Q27 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q27)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q27 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q28 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q28** {data-height=200}
```{r, echo = F}
summary(Q28)
```
### ONEWAY-test rezultati: **Q28** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q28 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q28, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q28** {data-height=200}
```{r, echo = F}
kruskal.test(Q28 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q28)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q28 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q29 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q29** {data-height=200}
```{r, echo = F}
summary(Q29)
```
### ONEWAY-test rezultati: **Q29** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q29 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q29, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q29** {data-height=200}
```{r, echo = F}
kruskal.test(Q29 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q29)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q29 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q30 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q30** {data-height=200}
```{r, echo = F}
summary(Q30)
```
### ONEWAY-test rezultati: **Q30** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q30 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q30, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q30** {data-height=200}
```{r, echo = F}
kruskal.test(Q30 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q30)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q30 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q31 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q31** {data-height=200}
```{r, echo = F}
summary(Q31)
```
### ONEWAY-test rezultati: **Q31** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q31 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q31, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q31** {data-height=200}
```{r, echo = F}
kruskal.test(Q31 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q31)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q31 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q32 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q32** {data-height=200}
```{r, echo = F}
summary(Q32)
```
### ONEWAY-test rezultati: **Q32** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q32 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q32, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q32** {data-height=200}
```{r, echo = F}
kruskal.test(Q32 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q32)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q32 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q33 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q33** {data-height=200}
```{r, echo = F}
summary(Q33)
```
### ONEWAY-test rezultati: **Q33** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q33 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q33, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q33** {data-height=200}
```{r, echo = F}
kruskal.test(Q33 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q33)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q33 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q35 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q35** {data-height=200}
```{r, echo = F}
summary(Q35)
```
### ONEWAY-test rezultati: **Q35** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q35 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q35, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q35** {data-height=200}
```{r, echo = F}
kruskal.test(Q35 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q35)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q35 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q36 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q36** {data-height=200}
```{r, echo = F}
summary(Q36)
```
### ONEWAY-test rezultati: **Q36** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q36 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q36, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q36** {data-height=200}
```{r, echo = F}
kruskal.test(Q36 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q36)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q36 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q37 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q37** {data-height=200}
```{r, echo = F}
summary(Q37)
```
### ONEWAY-test rezultati: **Q37** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q37 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q37, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q37** {data-height=200}
```{r, echo = F}
kruskal.test(Q37 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q37)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q37 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q38 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q38** {data-height=200}
```{r, echo = F}
summary(Q38)
```
### ONEWAY-test rezultati: **Q38** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q38 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q38, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q38** {data-height=200}
```{r, echo = F}
kruskal.test(Q38 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q38)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q38 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q39 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q39** {data-height=200}
```{r, echo = F}
summary(Q39)
```
### ONEWAY-test rezultati: **Q39** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q39 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q39, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q39** {data-height=200}
```{r, echo = F}
kruskal.test(Q39 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q39)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q39 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q40 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q40** {data-height=200}
```{r, echo = F}
summary(Q40)
```
### ONEWAY-test rezultati: **Q40** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q40 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q40, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q40** {data-height=200}
```{r, echo = F}
kruskal.test(Q40 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q40)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q40 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q41 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q41** {data-height=200}
```{r, echo = F}
summary(Q41)
```
### ONEWAY-test rezultati: **Q41** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q41 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q41, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q41** {data-height=200}
```{r, echo = F}
kruskal.test(Q41 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q41)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q41 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q42 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q42** {data-height=200}
```{r, echo = F}
summary(Q42)
```
### ONEWAY-test rezultati: **Q42** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q42 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q42, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q42** {data-height=200}
```{r, echo = F}
kruskal.test(Q42 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q42)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q42 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q43 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q43** {data-height=200}
```{r, echo = F}
summary(Q43)
```
### ONEWAY-test rezultati: **Q43** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q43 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q43, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q43** {data-height=200}
```{r, echo = F}
kruskal.test(Q43 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q43)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q43 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q44 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q44** {data-height=200}
```{r, echo = F}
summary(Q44)
```
### ONEWAY-test rezultati: **Q44** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q44 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q44, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q44** {data-height=200}
```{r, echo = F}
kruskal.test(Q44 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q44)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q44 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q45 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q45** {data-height=200}
```{r, echo = F}
summary(Q45)
```
### ONEWAY-test rezultati: **Q45** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q45 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q45, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q45** {data-height=200}
```{r, echo = F}
kruskal.test(Q45 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q45)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q45 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q46 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q46** {data-height=200}
```{r, echo = F}
summary(Q46)
```
### ONEWAY-test rezultati: **Q46** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q46 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q46, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q46** {data-height=200}
```{r, echo = F}
kruskal.test(Q46 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q46)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q46 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q47 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q47** {data-height=200}
```{r, echo = F}
summary(Q47)
```
### ONEWAY-test rezultati: **Q47** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q47 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q47, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q47** {data-height=200}
```{r, echo = F}
kruskal.test(Q47 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q47)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q47 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q48 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q48** {data-height=200}
```{r, echo = F}
summary(Q48)
```
### ONEWAY-test rezultati: **Q48** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q48 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q48, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q48** {data-height=200}
```{r, echo = F}
kruskal.test(Q48 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q48)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q48 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q49 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q49** {data-height=200}
```{r, echo = F}
summary(Q49)
```
### ONEWAY-test rezultati: **Q49** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q49 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q49, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q49** {data-height=200}
```{r, echo = F}
kruskal.test(Q49 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q49)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q49 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q50 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q50** {data-height=200}
```{r, echo = F}
summary(Q50)
```
### ONEWAY-test rezultati: **Q50** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q50 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q50, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q50** {data-height=200}
```{r, echo = F}
kruskal.test(Q50 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q50)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q50 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q51 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q51** {data-height=200}
```{r, echo = F}
summary(Q51)
```
### ONEWAY-test rezultati: **Q51** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q51 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q51, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q51** {data-height=200}
```{r, echo = F}
kruskal.test(Q51 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q51)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q51 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q52 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q52** {data-height=200}
```{r, echo = F}
summary(Q52)
```
### ONEWAY-test rezultati: **Q52** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q52 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q52, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q52** {data-height=200}
```{r, echo = F}
kruskal.test(Q52 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q52)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q52 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q53 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q53** {data-height=200}
```{r, echo = F}
summary(Q53)
```
### ONEWAY-test rezultati: **Q53** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q53 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q53, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q53** {data-height=200}
```{r, echo = F}
kruskal.test(Q53 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q53)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q53 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q58 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q58** {data-height=200}
```{r, echo = F}
summary(Q58)
```
### ONEWAY-test rezultati: **Q58** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q58 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q58, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q58** {data-height=200}
```{r, echo = F}
kruskal.test(Q58 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q58)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q58 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q59 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q59** {data-height=200}
```{r, echo = F}
summary(Q59)
```
### ONEWAY-test rezultati: **Q59** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q59 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q59, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q59** {data-height=200}
```{r, echo = F}
kruskal.test(Q59 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q59)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q59 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q60 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q60** {data-height=200}
```{r, echo = F}
summary(Q60)
```
### ONEWAY-test rezultati: **Q60** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q60 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q60, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q60** {data-height=200}
```{r, echo = F}
kruskal.test(Q60 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q60)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q60 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q61 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q61** {data-height=200}
```{r, echo = F}
summary(Q61)
```
### ONEWAY-test rezultati: **Q61** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q61 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q61, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q61** {data-height=200}
```{r, echo = F}
kruskal.test(Q61 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q61)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q61 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q62 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q62** {data-height=200}
```{r, echo = F}
summary(Q62)
```
### ONEWAY-test rezultati: **Q62** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q62 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q62, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q62** {data-height=200}
```{r, echo = F}
kruskal.test(Q62 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q62)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q62 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q63 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q63** {data-height=200}
```{r, echo = F}
summary(Q63)
```
### ONEWAY-test rezultati: **Q63** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q63 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q63, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q63** {data-height=200}
```{r, echo = F}
kruskal.test(Q63 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q63)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q63 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q64 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q64** {data-height=200}
```{r, echo = F}
summary(Q64)
```
### ONEWAY-test rezultati: **Q64** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q64 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q64, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q64** {data-height=200}
```{r, echo = F}
kruskal.test(Q64 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q64)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q64 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q65 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q65** {data-height=200}
```{r, echo = F}
summary(Q65)
```
### ONEWAY-test rezultati: **Q65** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q65 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q65, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q65** {data-height=200}
```{r, echo = F}
kruskal.test(Q65 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q65)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q65 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q66 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q66** {data-height=200}
```{r, echo = F}
summary(Q66)
```
### ONEWAY-test rezultati: **Q66** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q66 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q66, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q66** {data-height=200}
```{r, echo = F}
kruskal.test(Q66 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q66)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q66 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q67 {data-navmenu="ANOVA - po državama"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q67** {data-height=200}
```{r, echo = F}
summary(Q67)
```
### ONEWAY-test rezultati: **Q67** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q67 ~ podaci$Country)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q67, podaci$Country, center=mean)
```
### Kruskal-Wallis rezultati: **Q67** {data-height=200}
```{r, echo = F}
kruskal.test(Q67 ~ Country, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(Q67)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q67 ~ Country, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q1 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q1** {data-height=200}
```{r, echo = F}
summary(PQ1)
```
### ONEWAY-test rezultati: **Q1** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q1 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q1, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q1** {data-height=200}
```{r, echo = F}
kruskal.test(Q1 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ1)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q1 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q2 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q2** {data-height=200}
```{r, echo = F}
summary(PQ2)
```
### ONEWAY-test rezultati: **Q2** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q2 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q2, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q2** {data-height=200}
```{r, echo = F}
kruskal.test(Q2 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ2)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q2 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q3 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q3** {data-height=200}
```{r, echo = F}
summary(PQ3)
```
### ONEWAY-test rezultati: **Q3** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q3 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q3, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q3** {data-height=200}
```{r, echo = F}
kruskal.test(Q3 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ3)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q3 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q4 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q4** {data-height=200}
```{r, echo = F}
summary(PQ4)
```
### ONEWAY-test rezultati: **Q4** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q4 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q4, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q4** {data-height=200}
```{r, echo = F}
kruskal.test(Q4 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ4)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q4 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q5 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q5** {data-height=200}
```{r, echo = F}
summary(PQ5)
```
### ONEWAY-test rezultati: **Q5** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q5 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q5, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q5** {data-height=200}
```{r, echo = F}
kruskal.test(Q5 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ5)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q5 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q6 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q6** {data-height=200}
```{r, echo = F}
summary(PQ6)
```
### ONEWAY-test rezultati: **Q6** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q6 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q6, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q6** {data-height=200}
```{r, echo = F}
kruskal.test(Q6 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ6)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q6 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q7 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q7** {data-height=200}
```{r, echo = F}
summary(PQ7)
```
### ONEWAY-test rezultati: **Q7** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q7 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q7, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q7** {data-height=200}
```{r, echo = F}
kruskal.test(Q7 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ7)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q7 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q8 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q8** {data-height=200}
```{r, echo = F}
summary(PQ8)
```
### ONEWAY-test rezultati: **Q8** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q8 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q8, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q8** {data-height=200}
```{r, echo = F}
kruskal.test(Q8 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ8)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q8 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q9 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q9** {data-height=200}
```{r, echo = F}
summary(PQ9)
```
### ONEWAY-test rezultati: **Q9** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q9 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q9, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q9** {data-height=200}
```{r, echo = F}
kruskal.test(Q9 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ9)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q9 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q10 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q10** {data-height=200}
```{r, echo = F}
summary(PQ10)
```
### ONEWAY-test rezultati: **Q10** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q10 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q10, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q10** {data-height=200}
```{r, echo = F}
kruskal.test(Q10 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ10)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q10 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q11 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q11** {data-height=200}
```{r, echo = F}
summary(PQ11)
```
### ONEWAY-test rezultati: **Q11** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q11 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q11, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q11** {data-height=200}
```{r, echo = F}
kruskal.test(Q11 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ11)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q11 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q12 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q12** {data-height=200}
```{r, echo = F}
summary(PQ12)
```
### ONEWAY-test rezultati: **Q12** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q12 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q12, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q12** {data-height=200}
```{r, echo = F}
kruskal.test(Q12 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ12)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q12 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q13 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q13** {data-height=200}
```{r, echo = F}
summary(PQ13)
```
### ONEWAY-test rezultati: **Q13** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q13 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q13, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q13** {data-height=200}
```{r, echo = F}
kruskal.test(Q13 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ13)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q13 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q14 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q14** {data-height=200}
```{r, echo = F}
summary(PQ14)
```
### ONEWAY-test rezultati: **Q14** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q14 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q14, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q14** {data-height=200}
```{r, echo = F}
kruskal.test(Q14 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ14)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q14 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q15 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q15** {data-height=200}
```{r, echo = F}
summary(PQ15)
```
### ONEWAY-test rezultati: **Q15** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q15 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q15, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q15** {data-height=200}
```{r, echo = F}
kruskal.test(Q15 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ15)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q15 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q16 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q16** {data-height=200}
```{r, echo = F}
summary(PQ16)
```
### ONEWAY-test rezultati: **Q16** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q16 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q16, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q16** {data-height=200}
```{r, echo = F}
kruskal.test(Q16 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ16)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q16 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q17 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q17** {data-height=200}
```{r, echo = F}
summary(PQ17)
```
### ONEWAY-test rezultati: **Q17** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q17 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q17, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q17** {data-height=200}
```{r, echo = F}
kruskal.test(Q17 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ17)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q17 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q18 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q18** {data-height=200}
```{r, echo = F}
summary(PQ18)
```
### ONEWAY-test rezultati: **Q18** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q18 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q18, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q18** {data-height=200}
```{r, echo = F}
kruskal.test(Q18 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ18)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q18 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q19 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q19** {data-height=200}
```{r, echo = F}
summary(PQ19)
```
### ONEWAY-test rezultati: **Q19** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q19 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q19, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q19** {data-height=200}
```{r, echo = F}
kruskal.test(Q19 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ19)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q19 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q20 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q20** {data-height=200}
```{r, echo = F}
summary(PQ20)
```
### ONEWAY-test rezultati: **Q20** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q20 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q20, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q20** {data-height=200}
```{r, echo = F}
kruskal.test(Q20 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ20)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q20 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q21 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q21** {data-height=200}
```{r, echo = F}
summary(PQ21)
```
### ONEWAY-test rezultati: **Q21** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q21 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q21, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q21** {data-height=200}
```{r, echo = F}
kruskal.test(Q21 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ21)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q21 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q22 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q22** {data-height=200}
```{r, echo = F}
summary(PQ22)
```
### ONEWAY-test rezultati: **Q22** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q22 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q22, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q22** {data-height=200}
```{r, echo = F}
kruskal.test(Q22 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ22)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q22 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q23 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q23** {data-height=200}
```{r, echo = F}
summary(PQ23)
```
### ONEWAY-test rezultati: **Q23** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q23 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q23, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q23** {data-height=200}
```{r, echo = F}
kruskal.test(Q23 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ23)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q23 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q24 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q24** {data-height=200}
```{r, echo = F}
summary(PQ24)
```
### ONEWAY-test rezultati: **Q24** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q24 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q24, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q24** {data-height=200}
```{r, echo = F}
kruskal.test(Q24 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ24)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q24 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q26 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q26** {data-height=200}
```{r, echo = F}
summary(PQ26)
```
### ONEWAY-test rezultati: **Q26** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q26 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q26, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q26** {data-height=200}
```{r, echo = F}
kruskal.test(Q26 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ26)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q26 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q27 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q27** {data-height=200}
```{r, echo = F}
summary(PQ27)
```
### ONEWAY-test rezultati: **Q27** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q27 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q27, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q27** {data-height=200}
```{r, echo = F}
kruskal.test(Q27 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ27)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q27 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q28 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q28** {data-height=200}
```{r, echo = F}
summary(PQ28)
```
### ONEWAY-test rezultati: **Q28** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q28 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q28, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q28** {data-height=200}
```{r, echo = F}
kruskal.test(Q28 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ28)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q28 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q29 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q29** {data-height=200}
```{r, echo = F}
summary(PQ29)
```
### ONEWAY-test rezultati: **Q29** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q29 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q29, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q29** {data-height=200}
```{r, echo = F}
kruskal.test(Q29 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ29)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q29 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q30 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q30** {data-height=200}
```{r, echo = F}
summary(PQ30)
```
### ONEWAY-test rezultati: **Q30** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q30 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q30, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q30** {data-height=200}
```{r, echo = F}
kruskal.test(Q30 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ30)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q30 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q31 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q31** {data-height=200}
```{r, echo = F}
summary(PQ31)
```
### ONEWAY-test rezultati: **Q31** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q31 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q31, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q31** {data-height=200}
```{r, echo = F}
kruskal.test(Q31 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ31)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q31 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q32 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q32** {data-height=200}
```{r, echo = F}
summary(PQ32)
```
### ONEWAY-test rezultati: **Q32** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q32 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q32, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q32** {data-height=200}
```{r, echo = F}
kruskal.test(Q32 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ32)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q32 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q33 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q33** {data-height=200}
```{r, echo = F}
summary(PQ33)
```
### ONEWAY-test rezultati: **Q33** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q33 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q33, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q33** {data-height=200}
```{r, echo = F}
kruskal.test(Q33 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ33)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q33 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q35 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q35** {data-height=200}
```{r, echo = F}
summary(PQ35)
```
### ONEWAY-test rezultati: **Q35** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q35 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q35, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q35** {data-height=200}
```{r, echo = F}
kruskal.test(Q35 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ35)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q35 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q36 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q36** {data-height=200}
```{r, echo = F}
summary(PQ36)
```
### ONEWAY-test rezultati: **Q36** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q36 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q36, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q36** {data-height=200}
```{r, echo = F}
kruskal.test(Q36 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ36)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q36 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q37 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q37** {data-height=200}
```{r, echo = F}
summary(PQ37)
```
### ONEWAY-test rezultati: **Q37** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q37 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q37, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q37** {data-height=200}
```{r, echo = F}
kruskal.test(Q37 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ37)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q37 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q38 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q38** {data-height=200}
```{r, echo = F}
summary(PQ38)
```
### ONEWAY-test rezultati: **Q38** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q38 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q38, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q38** {data-height=200}
```{r, echo = F}
kruskal.test(Q38 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ38)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q38 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q39 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q39** {data-height=200}
```{r, echo = F}
summary(PQ39)
```
### ONEWAY-test rezultati: **Q39** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q39 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q39, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q39** {data-height=200}
```{r, echo = F}
kruskal.test(Q39 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ39)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q39 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q40 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q40** {data-height=200}
```{r, echo = F}
summary(PQ40)
```
### ONEWAY-test rezultati: **Q40** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q40 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q40, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q40** {data-height=200}
```{r, echo = F}
kruskal.test(Q40 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ40)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q40 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q41 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q41** {data-height=200}
```{r, echo = F}
summary(PQ41)
```
### ONEWAY-test rezultati: **Q41** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q41 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q41, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q41** {data-height=200}
```{r, echo = F}
kruskal.test(Q41 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ41)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q41 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q42 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q42** {data-height=200}
```{r, echo = F}
summary(PQ42)
```
### ONEWAY-test rezultati: **Q42** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q42 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q42, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q42** {data-height=200}
```{r, echo = F}
kruskal.test(Q42 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ42)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q42 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q43 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q43** {data-height=200}
```{r, echo = F}
summary(PQ43)
```
### ONEWAY-test rezultati: **Q43** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q43 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q43, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q43** {data-height=200}
```{r, echo = F}
kruskal.test(Q43 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ43)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q43 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q44 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q44** {data-height=200}
```{r, echo = F}
summary(PQ44)
```
### ONEWAY-test rezultati: **Q44** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q44 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q44, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q44** {data-height=200}
```{r, echo = F}
kruskal.test(Q44 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ44)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q44 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q45 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q45** {data-height=200}
```{r, echo = F}
summary(PQ45)
```
### ONEWAY-test rezultati: **Q45** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q45 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q45, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q45** {data-height=200}
```{r, echo = F}
kruskal.test(Q45 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ45)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q45 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q46 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q46** {data-height=200}
```{r, echo = F}
summary(PQ46)
```
### ONEWAY-test rezultati: **Q46** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q46 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q46, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q46** {data-height=200}
```{r, echo = F}
kruskal.test(Q46 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ46)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q46 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q47 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q47** {data-height=200}
```{r, echo = F}
summary(PQ47)
```
### ONEWAY-test rezultati: **Q47** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q47 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q47, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q47** {data-height=200}
```{r, echo = F}
kruskal.test(Q47 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ47)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q47 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q48 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q48** {data-height=200}
```{r, echo = F}
summary(PQ48)
```
### ONEWAY-test rezultati: **Q48** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q48 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q48, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q48** {data-height=200}
```{r, echo = F}
kruskal.test(Q48 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ48)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q48 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q49 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q49** {data-height=200}
```{r, echo = F}
summary(PQ49)
```
### ONEWAY-test rezultati: **Q49** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q49 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q49, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q49** {data-height=200}
```{r, echo = F}
kruskal.test(Q49 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ49)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q49 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q50 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q50** {data-height=200}
```{r, echo = F}
summary(PQ50)
```
### ONEWAY-test rezultati: **Q50** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q50 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q50, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q50** {data-height=200}
```{r, echo = F}
kruskal.test(Q50 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ50)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q50 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q51 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q51** {data-height=200}
```{r, echo = F}
summary(PQ51)
```
### ONEWAY-test rezultati: **Q51** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q51 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q51, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q51** {data-height=200}
```{r, echo = F}
kruskal.test(Q51 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ51)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q51 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q52 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q52** {data-height=200}
```{r, echo = F}
summary(PQ52)
```
### ONEWAY-test rezultati: **Q52** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q52 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q52, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q52** {data-height=200}
```{r, echo = F}
kruskal.test(Q52 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ52)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q52 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q53 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q53** {data-height=200}
```{r, echo = F}
summary(PQ53)
```
### ONEWAY-test rezultati: **Q53** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q53 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q53, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q53** {data-height=200}
```{r, echo = F}
kruskal.test(Q53 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ53)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q53 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q58 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q58** {data-height=200}
```{r, echo = F}
summary(PQ58)
```
### ONEWAY-test rezultati: **Q58** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q58 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q58, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q58** {data-height=200}
```{r, echo = F}
kruskal.test(Q58 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ58)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q58 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q59 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q59** {data-height=200}
```{r, echo = F}
summary(PQ59)
```
### ONEWAY-test rezultati: **Q59** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q59 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q59, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q59** {data-height=200}
```{r, echo = F}
kruskal.test(Q59 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ59)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q59 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q60 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q60** {data-height=200}
```{r, echo = F}
summary(PQ60)
```
### ONEWAY-test rezultati: **Q60** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q60 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q60, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q60** {data-height=200}
```{r, echo = F}
kruskal.test(Q60 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ60)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q60 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q61 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q61** {data-height=200}
```{r, echo = F}
summary(PQ61)
```
### ONEWAY-test rezultati: **Q61** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q61 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q61, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q61** {data-height=200}
```{r, echo = F}
kruskal.test(Q61 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ61)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q61 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q62 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q62** {data-height=200}
```{r, echo = F}
summary(PQ62)
```
### ONEWAY-test rezultati: **Q62** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q62 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q62, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q62** {data-height=200}
```{r, echo = F}
kruskal.test(Q62 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ62)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q62 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q63 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q63** {data-height=200}
```{r, echo = F}
summary(PQ63)
```
### ONEWAY-test rezultati: **Q63** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q63 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q63, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q63** {data-height=200}
```{r, echo = F}
kruskal.test(Q63 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ63)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q63 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q64 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q64** {data-height=200}
```{r, echo = F}
summary(PQ64)
```
### ONEWAY-test rezultati: **Q64** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q64 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q64, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q64** {data-height=200}
```{r, echo = F}
kruskal.test(Q64 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ64)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q64 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q65 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q65** {data-height=200}
```{r, echo = F}
summary(PQ65)
```
### ONEWAY-test rezultati: **Q65** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q65 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q65, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q65** {data-height=200}
```{r, echo = F}
kruskal.test(Q65 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ65)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q65 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q66 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q66** {data-height=200}
```{r, echo = F}
summary(PQ66)
```
### ONEWAY-test rezultati: **Q66** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q66 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q66, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q66** {data-height=200}
```{r, echo = F}
kruskal.test(Q66 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ66)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q66 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
Q67 {data-navmenu="ANOVA - po području"}
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### ANOVA rezultati: **Q67** {data-height=200}
```{r, echo = F}
summary(PQ67)
```
### ONEWAY-test rezultati: **Q67** {data-height=200}
```{r, echo = F}
oneway.test(podaci$Q67 ~ podaci$`Study field`)
```
### Levene test {data-height=250}
```{r, echo = F}
leveneTest(podaci$Q67, podaci$`Study field`, center=mean)
```
### Kruskal-Wallis rezultati: **Q67** {data-height=200}
```{r, echo = F}
kruskal.test(Q67 ~ `Study field`, data = podaci)
```
Row {data-width=200}
-----------------------------------------------------------------------
### Tukey multiple comparisons of means 95% family-wise confidence level
```{r, echo = F}
tablica <- as.data.frame(unclass(TukeyHSD(PQ67)))
colnames(tablica) <- c("diff", "lwr", "upr", "p adj")
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
### Dunn (1964) Kruskal-Wallis multiple comparison p-values adjusted with the Holm method.
```{r, echo = F}
tablica <- dunnTest(Q67 ~ `Study field`, data = podaci)$res
knitr::kable(tablica) %>% kable_classic("hover",full_width = F, html_font = "Cambria")
```
