##Opening libraries

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)

##Importing data

A6Q3_2 <- read_excel("A6Q3-2.xlsx")

##Descriptive statistics

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

##Histogram of Groups cardio & nocardio

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

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

##Histogram Report #Group 1: cardio The first variable looks normally distributed. The data is positively skewed. Data has a normal bell curve

#Group 2: nocardio The second variable looks normally distributed. The data is negatively skewed. The data has a proper bell curve.

##Boxplots of Groups (Exercise, Weight)

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

##Boxplot Report There are dots outside the boxplot. The dots are close to the whiskers. The dots are not far away from the whiskers. The outliers are balanced. Based on these findings, the boxplot is normal.

##Shapiro-WIlk Test

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

##Shapiro-Wilk test group report #cardio W = 0.96745, p-value = 0.5812

#nocardio W = 0.97686, p-value = 0.8166

#Group 1: cardio The first group is normally distributed, (p = .058).

#Group 2: nocardio The second group is normally distributed, (p = .081).

Data is normal.

##T-test

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

##Summary of Group tested. An Independent T-Test was conducted to determine if there was a difference in Weight between nocardio and cardio groups. nocardio scores (M = 74.73, SD = 7.57) were not significantly different from cardio scores (M = 70.81, SD = 7.35), t(48) = 1.855, p = >.05.