A6Q32

#install.packages("dplyr")
#install.packages("effectsize")
#install.packages("effsize")
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)
A6Q32 <- read_excel("C:/Users/sanja/Downloads/A6Q3-2.xlsx")
A6Q32 %>%
  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(A6Q32$Weight[A6Q32$Exercise == "cardio"],
     main = "Histogram of Cardio Group (Weight)",
     xlab = "Weight (kg)",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q32$Weight[A6Q32$Exercise == "nocardio"],
     main = "Histogram of No Cardio Group (Weight)",
     xlab = "Weight (kg)",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

#Group 1: cardio
#The first variable looks [normally] distributed.
#The data is [symmetrical].
#The data [has] a proper bell curve.


#Group 2: nocardio
#The second variable looks [normally] distributed.
#The data is [symmetrical].
#The data [has] a proper bell curve.
ggboxplot(A6Q32, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

#Boxplot 1: cardio
#There [are] dots outside the boxplot.
#The dots [are] close to the whiskers.
#The dots [are not] very far away from the whiskers.
#The outliers [are] balanced.
#Based on these findings, the boxplot is [normal]

#Boxplot 2: nocardio
#There [are] dots outside the boxplot.
#The dots [are] close to the whiskers.
#The dots [are not] very far away from the whiskers.
#The outliers [are] balanced.
#Based on these findings, the boxplot is [normal]
shapiro.test(A6Q32$Weight[A6Q32$Exercise == "cardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q32$Weight[A6Q32$Exercise == "cardio"]
## W = 0.96745, p-value = 0.5812

shapiro.test(A6Q32$Weight[A6Q32$Exercise == "nocardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q32$Weight[A6Q32$Exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166

#Group 1: cardio
#The first group is [normally] distributed, (p = .581).

#Group 2: nocardio
#The second group is [normally] distributed, (p = .817).
t.test(Weight ~ Exercise, data = A6Q32, 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

#An Independent T-Test was conducted to determine if there was a difference in Weight between cardio and nocardio.
#Group 1 scores (M = 74.7 SD =7.57 ) were [not significantly] different from Group2 scores (M = 70.8, SD = 7.35), t(48) = 1.86, p >.05