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library(readxl)
library(ggpubr)
## Loading required package: ggplot2
#library(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(rstatix)
## 
## Attaching package: 'rstatix'
## The following objects are masked from 'package:effectsize':
## 
##     cohens_d, eta_squared
## The following object is masked from 'package:stats':
## 
##     filter
A6Q3<-read_excel("A6Q3-1.xlsx")
A6Q3
## # A tibble: 50 × 2
##    exercisetype bodyweight
##    <chr>             <dbl>
##  1 nocardio           56.5
##  2 nocardio           76.7
##  3 nocardio           71.2
##  4 nocardio           60.9
##  5 nocardio           80.0
##  6 nocardio           73.4
##  7 nocardio           67.6
##  8 nocardio           77.2
##  9 nocardio           77.0
## 10 nocardio           76.6
## # ℹ 40 more rows
A6Q3 %>%
  group_by(exercisetype) %>%
  summarise(
    Mean = mean(bodyweight, na.rm = TRUE),
    Median = median(bodyweight, na.rm = TRUE),
    SD = sd(bodyweight, na.rm = TRUE),
    N = n()
  )
## # A tibble: 2 × 5
##   exercisetype  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(A6Q3$bodyweight[A6Q3$exercisetype == "nocardio"],
     main = "Histogram of nocardio bodyweight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q3$bodyweight[A6Q3$exercisetype == "cardio"],
     main = "Histogram of Cardio bodyweight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

#Group 1: No Cardio #The first variable looks normally distributed. #The data is symmetrical. #The data has a proper bell curve.

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

ggboxplot(A6Q3, x = "exercisetype", y = "bodyweight",
          color = "exercisetype",
          palette = "jco",
          add = "jitter")

#Boxplot 1: No cardio #There are dots outside the boxplot. #The dot is close to the whisker. #Based on these findings, the boxplot is normal.

#Boxplot 2: Cardio #There are dots outside the boxplot. #The dots are close to the whiskers. #Based on these findings, the boxplot is not normal.

shapiro.test(A6Q3$bodyweight[A6Q3$exercisetype == "nocardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$bodyweight[A6Q3$exercisetype == "nocardio"]
## W = 0.97686, p-value = 0.8166
shapiro.test(A6Q3$bodyweight[A6Q3$exercisetype == "cardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$bodyweight[A6Q3$exercisetype == "cardio"]
## W = 0.96745, p-value = 0.5812

#Group 1: No cardio #The first group is normally distributed, (p = .81).

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

t.test(bodyweight ~ exercisetype, data = A6Q3, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  bodyweight by exercisetype
## 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
wilcox.test(bodyweight ~ exercisetype, data = A6Q3)
## 
##  Wilcoxon rank sum exact test
## 
## data:  bodyweight by exercisetype
## W = 399, p-value = 0.09541
## alternative hypothesis: true location shift is not equal to 0

#An Independent T-Test was conducted to determine if there was a difference in bodyweight between no lift and lift. #cardio (M = 70.81, SD = 7.50) were significantly different from no cardio (M = 74.73, SD = 7.3), t(48) = 1.8, p = .09.

#A Mann-Whitney U test was conducted to determine if there was a difference in bodyweight between cardio and no cardio. #cardio (Mdn = 73.25620) were significantly different from no cardio (Mdn = 69.50471), U = 399, p = .09.

#https://rpubs.com/jishimwe1/1426232