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)
A6Q3_2 <- read_excel("C:/Users/edavi/OneDrive/Desktop/A6Q3-2.xlsx")
View(A6Q3_2)
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

A tibble: 2 × 5 Exercise Mean Median SD N 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=="nocardio"],main="Histogram of No Cardio Weight",xlab="Value",ylab="Frequency",col="lightblue",border="black",breaks=10)

hist(A6Q3_2$Weight[A6Q3_2$Exercise=="cardio"],main="Histogram of Cardio Weight",xlab="Value",ylab="Frequency",col="lightgreen",border="black",breaks=10)

Group 1: No Cardio Weight The first variable looks normally distributed. The data is symmetrical. The data does have a proper bell curve.

Group 2: Cardio Weight The second variable looks normally distributed. The data is symmetrical. The data has a proper bell curve.

ggboxplot(A6Q3_2,x="Exercise",y="Weight",color="Exercise",palette="jco",add="jitter")

Corrected my interpretation to show that dots WERE close to the whiskers. Boxplot 1: nocardio There are dots outside the boxplot. The dots are close to the whiskers. Based on these findings, the boxplot is normal.

Corrected my interpretation to show that the dots WERE close to the whiskers. Boxplot 2: cardio There are dots outside the boxplot. The dots are close to the whiskers. Based on these findings, the boxplot is normal.

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

Shapiro-Wilk normality test

data: A6Q3_2\(Weight[A6Q3_2\)Exercise == “nocardio”] W = 0.97686, p-value = 0.8166

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-Wilk normality test

data: A6Q3_2\(Weight[A6Q3_2\)Exercise == “cardio”] W = 0.96745, p-value = 0.5812

Group 1: No Cardio The first group is normally distributed, (p = .817).

Group 2: Cardio The second group is normally distributed, (p = .581)

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

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

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]

Corrected my report to account for a rounding error An Independent T-Test was conducted to determine if there was a difference in Weight between no cardio and cardio. No cardio scores (M = 70.8, SD = 7.35) were not significantly different from cardio scores (M = 74.7, SD = 7.57), t=(48) = 1.86, p > .05.