A6Q3

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 <- read_excel("C:/Users/laksh/Downloads/A6Q3.xlsx")
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 == "nocardio"],
     main = "Histogram of No cardio Exercise",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

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

Group 1: 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, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

Boxplot 1: No Cardio 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 not normal.

Boxplot 2: Cardio 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 balanced. Based on these findings, the boxplot is normal.

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

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

Group 1: nocardio The first group is normally distributed, (p = .817).

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

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

An Independent T-Test was conducted to determine if there was a difference in Weight between Cardio and No Cardio. Cardio scores (M = 72.7, SD = 7.57) were significantly different from No cardio (M = 70.8, SD = 7.35), t(48) = 1.855, p = .07