```{r} library(readxl) library(ggpubr) library(effsize) library(rstatix) A6Q1 <- read_excel(“C:/Users/User/Downloads/A6Q1.xlsx”) Before <- A6Q1\(Before After <- A6Q1\)After

Differences <- After - Before mean(Before, na.rm = TRUE) median(Before, na.rm = TRUE) sd(Before, na.rm = TRUE)

mean(After, na.rm = TRUE) median(After, na.rm = TRUE) sd(After, na.rm = TRUE) hist(Differences, breaks = 15, col = “blue”, border = “white”) #Data for the difference scores apperas normally distributed. boxplot(Differences, main = “Distribution of Score Differences (After - Before)”, ylab = “Difference in Scores”, col = “blue”, border = “darkblue”) # The difference scores boxplot does not have outliers. shapiro.test(Differences) #Shapiro-Wilk Difference Scores #The data is normally distributed, (p = .33). t.test(Before, After, paired = TRUE, na.action = na.omit) cohen.d(Before, After, paired = TRUE) # A Dependent T-Test was conducted to determine if there was a difference in Weight (kg) between Before a low-calorie diet and After a low-calorie diet. ## Here, I should have specified the unit (kg) and that the before and after was before and after a low-calorie diet rather than just stating “between before and after”. #Before scores (M = 76.13, SD = 7.78) were significantly from After scores (M = 71.59, SD = 6.64), (t(19) = 1.90, p < .05.) ##I listed p as p=.072 rather than p<.05. I did not have to write out the p value here. #The effect size was large, Cohen’s d = .63. ##I should have rounded to 2 decimal points here. I wrote .628 but the correct answer was .63 ```