library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(effectsize)
library(effsize)
A6Q4 <- read_excel("C:/Users/aleat/Downloads/A6Q4-2.xlsx")
View(A6Q4)
A6Q4 %>% group_by(Exercise) %>% summarise(Mean = mean(Weight, na.rm = TRUE), Median = median(Weight, na.rm = TRUE), SD = sd(Weight, na.rm = TRUE), N = n())
## # A tibble: 2 × 5
##   Exercise  Mean Median    SD     N
##   <chr>    <dbl>  <dbl> <dbl> <int>
## 1 lift     120.   116.   53.3    25
## 2 nolift    33.0   40.8  56.7    25
hist(A6Q4$Weight[A6Q4$Exercise == "nolift"],
     main = "Histogram of No Lifting Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q4$Weight[A6Q4$Exercise == "lift"],
     main = "Histogram of Lifting Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

Group 1: No Lifting The first variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

Group 2: Lifting The second variable looks abnormally distributed. The data is positively skewed. The data does not have a proper bell curve.

ggboxplot(A6Q4, x = "Exercise", y = "Weight", color = "Exercise", palette = "jco", add = "jitter")

Boxplot 1: No Lifting There are dots outside the boxplot. One dot is not close to the whiskers. The dot is very far away from the whiskers. The outliers are not balanced. Based on these findings, the boxplot is not normal.

Boxplot 2: Lifting There are dots outside the boxplot. Two dots are not close to the whiskers. Two 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$Weight[A6Q4$Exercise == "nolift"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4$Weight[A6Q4$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06
shapiro.test(A6Q4$Weight[A6Q4$Exercise == "lift"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4$Weight[A6Q4$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436

Group 1: No Lifting The first group is abnormally distributed, (p = 7.294e-06).

Group 2: Lifting The second group is abnormally distributed, (p = .0001436).

wilcox.test(Weight ~ Exercise, data = A6Q4)
## 
##  Wilcoxon rank sum exact test
## 
## data:  Weight by Exercise
## W = 603, p-value = 7.132e-11
## alternative hypothesis: true location shift is not equal to 0
mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q4)
print(mw_effect)
## 
## Cliff's Delta
## 
## delta estimate: 0.9296 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.7993841 0.9764036

A Mann-Whitney U test was conducted to determine if there was a difference in weight between those who do not lift weights and those who do lift weights. No Lifting scores (Mdn = 40.80) were significantly different from Lifting scores (Mdn = 116.00), U = 603.00, p = 7.132e-11. The effect size was large, Cliff’s Delta = .93.