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("Downloads/A6Q4.xlsx")
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 == "lift"],
     main = "Histogram of Lift Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "maroon",
     border = "black",
     breaks = 10)

hist(A6Q4$Weight[A6Q4$Exercise == "nolift"],
     main = "Histogram of Nolift Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     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 2: nolift The second variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

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

Boxplot 1: nolift There is one dot outside the boxplot. The 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: 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 not normal.

shapiro.test(A6Q4$Weight[A6Q4$Exercise == "lift"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4$Weight[A6Q4$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436
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

Group 1: lift The first group is abnormally distributed, (p = .0001436).

Group 2: nolift The second group is abnormally distributed, (p = .000007294).

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 body weight (kg) between participants who lift weights versus participants who do not lift weights. Lift weights (Mdn = 116.00) were significantly different from nolift weights (Mdn = 40.80) U = 603.00, p < .001. The effect size was large, Cliff’s Delta = .93.