A6Q4

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)

A6Q4xlsx <- read_excel("A6Q4xlsx.xlsx")

A6Q4xlsx%>%
  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(A6Q4xlsx$Weight[A6Q4xlsx$Exercise== "nolift"],
     main = "Histogram of nolift",
     xlab = "Exercise",
     ylab = "Weight",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q4xlsx$Weight[A6Q4xlsx$Exercise== "lift"],
     main = "Histogram of lift",
     xlab = "Exercise",
     ylab = "Weight",
     col = "lightgreen",
     border = "black",
     breaks = 10)

#Group 1: lift
#The first variable looks abnormally distributed.
#The data is positively skewed.
#The data does not have a proper bell curve.


#Group 2: nolift
#The second variable looks abnormally distributed.
#The data is negatively skewed.
#The data does not have a proper bell curve. 

ggboxplot(A6Q4xlsx, x = "Exercise", y = "Weight",
          color = "blue",
          palette = "jco",
          add = "jitter") 

#Boxplot 1: nolife 
#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 not balanced.
#Based on these findings, the boxplot is normal

#Boxplot 2: lift
#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 not balanced.
#Based on these findings, the boxplot is normal

shapiro.test(A6Q4xlsx$Weight[A6Q4xlsx$Exercise == "nolift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4xlsx$Weight[A6Q4xlsx$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06

shapiro.test(A6Q4xlsx$Weight[A6Q4xlsx$Exercise == "lift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4xlsx$Weight[A6Q4xlsx$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436

#Group 1: nolift
#The first group is normally distributed, (p = 7.294e-06).

#Group 2: lift
#The second group is abnormally distributed, (p = .0.1436).