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)
A6Q3_2 <- read_excel("C:/Users/Jasmine/Desktop/AA521 Week 6/A6Q3-2.xlsx")
A6Q3_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 cardio 74.7 73.3 7.57 25
## 2 nocardio 70.8 69.5 7.35 25
hist(A6Q3_2$Weight[A6Q3_2$Exercise == "cardio"],
main = "Histogram of Cardio Weight",
xlab = "Value",
ylab = "Frequency",
col = "lightblue",
border = "black",
breaks = 10)

hist(A6Q3_2$Weight[A6Q3_2$Exercise == "nocardio"],
main = "Histogram of No Cardio Weight",
xlab = "Value",
ylab = "Frequency",
col = "lightgreen",
border = "black",
breaks = 10)

#Group 1: Cardio
#The first variable looks normally distributed.
#The data is roughly symmetrical (skewness = 0.29).
#The data has a proper bell curve (kurtosis = 2.64, close to the normal value of 3).
#Group 2: No Cardio
#The second variable looks normally distributed.
#The data is symmetrical (skewness = 0.05).
#The data has a proper bell curve (kurtosis = 2.57, close to the normal value of 3).
ggboxplot(A6Q3_2, x = "Exercise", y = "Weight",
color = "Exercise",
palette = "jco",
add = "jitter")

#Boxplot 1: Cardio
#There are no dots outside the boxplot.
#Based on these findings, the boxplot is normal.
#Boxplot 2: No Cardio
#There are no dots outside the boxplot.
#Based on these findings, the boxplot is normal.
shapiro.test(A6Q3_2$Weight[A6Q3_2$Exercise == "cardio"])
##
## Shapiro-Wilk normality test
##
## data: A6Q3_2$Weight[A6Q3_2$Exercise == "cardio"]
## W = 0.96745, p-value = 0.5812
shapiro.test(A6Q3_2$Weight[A6Q3_2$Exercise == "nocardio"])
##
## Shapiro-Wilk normality test
##
## data: A6Q3_2$Weight[A6Q3_2$Exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166
#Group 1: Cardio
#The first group is normally distributed, (p = .581).
#Group 2: No Cardio
#The second group is normally distributed, (p = .817).
t.test(Weight ~ Exercise, data = A6Q3_2, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Weight by Exercise
## t = 1.8552, df = 48, p-value = 0.06971
## alternative hypothesis: true difference in means between group cardio and group nocardio is not equal to 0
## 95 percent confidence interval:
## -0.3280454 8.1605622
## sample estimates:
## mean in group cardio mean in group nocardio
## 74.73336 70.81710
wilcox.test(Weight ~ Exercise, data = A6Q3_2)
##
## Wilcoxon rank sum exact test
##
## data: Weight by Exercise
## W = 399, p-value = 0.09541
## alternative hypothesis: true location shift is not equal to 0
cohens_d_result <- cohens_d(Weight ~ Exercise, data = A6Q3_2, pooled_sd = TRUE)
print(cohens_d_result)
## Cohen's d | 95% CI
## -------------------------
## 0.52 | [-0.04, 1.09]
##
## - Estimated using pooled SD.
mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q3_2)
print(mw_effect)
##
## Cliff's Delta
##
## delta estimate: 0.2768 (small)
## 95 percent confidence interval:
## lower upper
## -0.05295739 0.55212525
#An Independent T-Test was conducted to determine if there was a difference in
#Weight between Cardio and No Cardio groups.
#Cardio scores (M = 74.73, SD = 7.57) were not significantly different from
#No Cardio scores (M = 70.82, SD = 7.35), t(48) = 1.86, p > .05.
#A Mann-Whitney U test was conducted to determine if there was a difference in
#Weight between Cardio and No Cardio groups.
#Cardio scores (Mdn = 73.26) were not significantly different from No Cardio
#scores (Mdn = 69.50), U = 399.00, p > .05.