A6Q3 Isabel Tillett

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

library(rmarkdown)
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)

A6Q3 <- read_excel("A6Q3-2.xlsx")
#CORRECTION: I corrected the file path and dataset filename so the dataset imports properly.

A6Q3 %>%
  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(
  A6Q3$Weight[A6Q3$Exercise == "cardio"],
  main = "Histogram of Cardio Weight",
  xlab = "Value",
  ylab = "Frequency",
  col = "lightblue",
  border = "black",
  breaks = 10
)

#CORRECTION: I corrected the cardio histogram title, x-axis label, color, and number of breaks.
#Cardio Group interpretation
#The data is normally distributed.
#The data is symmetrical.
#The data has a proper bell curve.

hist(
  A6Q3$Weight[A6Q3$Exercise == "nocardio"],
  main = "Histogram of NoCardio Weight",
  xlab = "Value",
  ylab = "Frequency",
  col = "lightgreen",
  border = "black",
  breaks = 10
)

#CORRECTION: I corrected the no cardio histogram title, x-axis label, color, and number of breaks.

#No Cardio Group Interpretation
#The data is normally distributed.
#The data is symmetrical.
#The data has a proper bell curve.

ggboxplot(A6Q3, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

#CORRECTION: I corrected the boxplot code to match the exercise-group boxplot with jittered points.


#Boxplot 1:Cardio Group
#The cardio boxplot has dots outside the boxplot.
#The dots are close to the whiskers.
#The boxplot is normal.

#Boxplot 2: No Cardio Group
#The nocardio boxplot has dots outside the boxplot.
#The dots are close to the whiskers
#The boxplot is normal.

shapiro.test(A6Q3$Weight[A6Q3$Exercise == "cardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$Weight[A6Q3$Exercise == "cardio"]
## W = 0.96745, p-value = 0.5812

shapiro.test(A6Q3$Weight[A6Q3$Exercise == "nocardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$Weight[A6Q3$Exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166

#Shapiro-Wilk Test: Cardio Group
#The data is normally distributed (p = .58).

#Shapiro-Wilk Test: No Cardio Group
#The data is normally distributed (p = .82).
#CORRECTION: I updated the Shapiro-Wilk interpretations to include the exact rounded p-values.

t.test(Weight ~ Exercise,
       data = A6Q3,
       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

#Independent Samples T-Test
#There is not a statistically significant difference in body weight between participants who do cardio and participants who do not do cardio.
#t(48) = 1.86, p > .05.
#We fail to reject the null hypothesis.

#CORRECTION: I corrected the mean values and wording in my final report.
#An Independent T-Test was conducted to determine if there was a difference in weight (kg) between participants that did cardio versus those that did not do cardio.
#The weight of the group that did cardio (M = 74.70, SD = 7.57) was not significantly different from the group that did not do cardio (M = 70.80, SD = 7.35), t(48) = 1.86, p > .05.