#Is there a difference in body weight (kg) between participants who lift weights versus participants who do not lift weights?

library(readxl)
library(ggpubr)
library(dplyr)
library(effectsize)
library(effsize)
A6Q4 <- read_excel("/Users/murphy/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 == "nolift"],
     main = "Histogram of Weight (kg) of participants who do not lift weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

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

Group 1: Do not lift Weights The first variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

Group 2: Lift Weights 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: Do not lift Weights 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.

Boxplot 2: Lift Weights 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 == "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 Lift The first group is abnormally distributed, (p < .001).

Group 2: Lift The second group is abnormally distributed, (p < .001).

For both groups, I reported the p value as the number returned in the Shapiro-Wilk normality test. Corrected it so it shows p < .001 as both numbers are less than .001.

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 in body weight (kg) between participants who lift weights versus participants who do not lift weights. Participants who lift weights (Mdn = 116.00) were significantly different from participants who do not lift weights (Mdn = 40.80) U = 603, p < .001. The effect size was large, Cliff’s Delta = 0.930.

Removed the numbers after the decimal point for the U value in the closing statement.