A6Q421

#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)
A6Q421 <- read_excel("C:/Users/sanja/Downloads/A6Q4-2 (1).xlsx")
A6Q421 %>%
  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(A6Q421$Weight[A6Q421$Exercise == "lift"],
     main = "Histogram of Lift Group (Weight)",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q421$Weight[A6Q421$Exercise == "nolift"],
     main = "Histogram of No Lift Group (Weight)",
     xlab = "Value",
     ylab = "Frequency",
     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(A6Q421, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

#Boxplot 1: No lift
#There [are] dots outside the boxplot.
#The dots [are not] close to the whiskers.
#The dots [are] 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 not] close to the whiskers.
#The dots [are] very far away from the whiskers.
#The outliers [are not] balanced.
#Based on these findings, the boxplot is [normal]
shapiro.test(A6Q421$Weight[A6Q421$Exercise == "lift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q421$Weight[A6Q421$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436

shapiro.test(A6Q421$Weight[A6Q421$Exercise == "nolift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q421$Weight[A6Q421$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06

#Group 1: Lift
#The first group is [abnormally] distributed, (p < .05).

#Group 2: No lift
#The second group is [abnormally] distributed, (p < .05).
wilcox.test(Weight ~ Exercise, data = A6Q421)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Weight by Exercise
## W = 603, p-value = 7.132e-11
## alternative hypothesis: true location shift is not equal to 0

mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q421)
print(mw_effect)

## 
## Cliff's Delta
## 
## delta estimate: 0.9296 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.7993841 0.9764036

#A Mann-Whitney U test was conducted to determine if there was a difference in OutcomeVariable between Group1 and Group2.
#lift  group scores  (Mdn = 116) were [significantly] different from Nolift Group scores (Mdn = 41) U = 603, p < .001.
#The effect size was [very large], Cliff's Delta = 0.93.