library(readxl) library(ggpubr)
library(dplyr) library(rmarkdown) library(effectsize) library(effsize)
A6Q4_2 %>% group_by(Exercise) %>% summarise( mean = mean(Weight, na.rm = TRUE), medidan = median(Weight, na.rm = TRUE), sd = sd(Weight, na.rm = TRUE) )
hist(A6Q4_2\(Weight[A6Q4_2\)Exercise == “lift”], main = “Histogram of Lift Weight”, xlab = “Value”, ylab = “Frequency”, col = “white”, border = “blue”, breaks = 10)
hist(A6Q4_2\(Weight[A6Q4_2\)Exercise == “nolift”], main = “Histogram of no lift Weight”, xlab = “Value”, ylab = “Frequency”, col = “yellow”, border = “blue”, breaks = 10)
#Group 1: lift #The first variable looks abnormally distributed. #The data is positively skewed #The data does not have a proper bell curve.
#Group 1: nolift #The first variable looks abnormally distributed. #The data is negatively skewed #The data does not have a proper bell curve.
ggboxplot(A6Q4_2, x = “exercise”, y = “Weight”, color = “exercise”, palette = “jco”, add = “jitter”)
ggboxplot(A6Q4_2, x = “nolift”, y = “Weight”, color = “lift”, palette = “jco”, add = “jitter”)
#Boxplot 1: lift #There are dots outside the boxplot. #The dots are not close to the whiskers. #The dots are very far away from the whiskers. #The outliers are not balanced. #Based on these findings, the boxplot is normal
#Boxplot 2: nolift #There are dots outside the boxplot. #The dots are not close to the whiskers. #The dots are very far away from the whiskers. #The outliers are not balanced. #Based on these findings, the boxplot is not normal
shapiro.test(A6Q4_2\(Weight[A6Q4_2\)Exercise == “lift”]) shapiro.test(A6Q4_2\(Weight[A6Q4_2\)Exercise == “nolift”])
#Group 1: lift #The first group is abnormally distributed, (p=.000)
#Group 2: No lift #The second group is abnormally distributed, (p=7.294e-06)
t.test(excercise ~ lift, data = A6Q4_2, var.equal = TRUE) t.test(excercise ~ nolift, data = A6Q4_2, var.equal = TRUE)
wilcox.test(weight ~ lift, data = A6Q4_2) wilcox.test(weight ~ nolift, data = A6Q4_2)
cohens_d_result <- cohen.d(weight ~ lift, data = A6Q4_2, pooled_sd = TRUE)
cohen.d(weight ~ nolift, data = A6Q4_2, pooled_sd = TRUE)
#cliff’s delta
#effect size mw_effect <- cliff.delta(weight ~ excercise, data = A6Q4_2) print(mw_effect)
#An Independent T-Test was conducted to determine if there was a difference in weight between lift and nolift. #Group 1 scores (M = 120, SD = 53.3) were not significantly different from Group2 scores (M = 33, SD = 56.7), t(49) #The effect size was large
#A mann-whitney U test was conducted to determine if there was a difference in weight between lift and nolift. #Group 1 scores (Mdn = 116) were significantly different from Group2 scores (Mdn = 40.8) #The effect size was large, cliff’s delta = .93 ```