A6Q3

Is there a difference in mean body weight (kg) between participants who do cardio versus participants who do not do cardio?

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)

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

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

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

Group1: NoCardio 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_2, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

Boxplot 1: NoCardio There are dots outside the boxplot. The dots are close to the whiskers. The dots are very far away from the whiskers. The outliers qre not balanced. Based on these findings, the boxplot is not normal.

Boxplot 2: Cardio There are dots outside the boxplot. The dots are close to the whiskers. The dots are very far away from the whiskers. The outliers are balanced. Based on these findings, the boxplot is normal.

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.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

Group 1: Name of noncardio The first group is normally distributed, (p = .8166). Group 2: Name of Second Group The second group is normally distributed, (p = .5812).

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

cohens_d_result <- cohens_d(Weight ~ Exercise, data = A6Q3_2, pooled_sd = TRUE)
print(cohens_d_result)

## Cohen's d |        95% CI
## -------------------------
## 0.52      | [-0.04, 1.09]
## 
## - Estimated using pooled SD.

An Independent T-Test was conducted to determine if there was a difference in Weight between nocardio and cardio exercise. nocardio (M = 70.8, SD = 7.35) were significantly different from cardio scores (M = 74.7, SD = 7.57), t(48) = 1.85, p = .06971. The effect size was medium, Cohen’s d = .52.