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)
library(readxl)
A6Q4_2 <- read_excel("C:/Users/jrutebuka/Desktop/A6Q4-2.xlsx")
A6Q4_2 %>%
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_2$Weight [A6Q4_2$Exercise == "nolift"],
main = "Histogram of nolift",
xlab = "Value",
ylab = "Frequency",
col = "lightgreen",
border = "black",
breaks = 10)
hist(A6Q4_2$Weight [A6Q4_2$Exercise == "lift"],
main = "Histogram of lift",
xlab = "Value",
ylab = "Frequency",
col = "lightgreen",
border = "black",
breaks = 10)
#Group 1: nolift
#The first variable looks abnormally distributed.
#The data is positively skewed.
#The data doesn't have bell curve, there is a tail instead.
#Group 1: lift
#The first variable looks abnormally distributed.
#The data is negatively skewed.
#The data doesn't have bell curve, there is a tail instead.
ggboxplot(A6Q4_2, x = "Exercise", y = "Weight",
color = "Exercise",
palette = "jco",
add = "jitter")
#Boxplot 1: 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
#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 not normal
shapiro.test(A6Q4_2$Weight[A6Q4_2$Exercise == "nolift"])
##
## Shapiro-Wilk normality test
##
## data: A6Q4_2$Weight[A6Q4_2$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06
shapiro.test(A6Q4_2$Weight[A6Q4_2$Exercise == "lift"])
##
## Shapiro-Wilk normality test
##
## data: A6Q4_2$Weight[A6Q4_2$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436
#nolift
#The first group is abnormally distributed, (p <0.01).
#lift
#The second group is abnormally distributed, (p <0.01).
t.test(Weight ~ Exercise, data = A6Q4_2, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Weight by Exercise
## t = 5.5923, df = 48, p-value = 1.045e-06
## alternative hypothesis: true difference in means between group lift and group nolift is not equal to 0
## 95 percent confidence interval:
## 55.75715 118.35710
## sample estimates:
## mean in group lift mean in group nolift
## 120.08238 33.02525
wilcox.test(Weight ~ Exercise, data = A6Q4_2)
##
## 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
cohens_d_result <- cohen.d(Weight ~ Exercise, data = A6Q4_2, pooled_sd = TRUE)
print(cohens_d_result)
##
## Cohen's d
##
## d estimate: 1.581752 (large)
## 95 percent confidence interval:
## lower upper
## 0.9301721 2.2333327
mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q4_2)
print(mw_effect)
##
## Cliff's Delta
##
## delta estimate: 0.9296 (large)
## 95 percent confidence interval:
## lower upper
## 0.7993841 0.9764036
#An Independent T-Test was conducted to determine if there was a difference in body Weight between Participants who Lift and Participants who do not Lift.
#Weight of Lift (M = 120, SD = 53.3) were significantly different from Body weight of no lift (M = 33.0, SD = 56.7), t(48) = 5.59, p < 0.01.
#The effect size was large, Cohen's d = 1.58.
#A Mann-Whitney U test was conducted to determine if there was a difference in Weight between Participants who Lift and Participants who do not Lift
#nolift (Mdn = 40.8) were significantly different from lift (Mdn = 116.0 ) U = 0.97, p <0.01.
#The effect size was large, Cliff's Delta = 0.929.