A6Q3

Load library

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)
library(tidyverse)

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## ✔ purrr     1.2.1     ✔ tidyr     1.3.2
## ✔ readr     2.2.0

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## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

load dataset

D3 <- read_excel("A6Q3.xlsx")

#Descriptive Statistics

D3 <- pivot_longer(D3,cols = c(nocardio, cardio), names_to = "label", values_to = "weight") 
D3 %>%
  group_by(label) %>%
  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
##   label     Mean Median    SD     N
##   <chr>    <dbl>  <dbl> <dbl> <int>
## 1 cardio    70.8   69.5  7.35    25
## 2 nocardio  74.7   73.3  7.57    25

Histogram Creation

hist(D3$weight[D3$label == "nocardio"],
     main = "Histogram of No Cardio Weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "pink",
     breaks = 10)

hist(D3$weight[D3$label == "cardio"],
     main = "Histogram of cardio weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "pink",
     breaks = 10)

Group 1: nocardio

The first variable looks normally distributed. The data is positively sqewed. The data has a proper bell curve.

Group 2: Female

The second variable looks normally distributed. The data is negatively skewed. The data has a proper bell curve.

Box plots

ggboxplot(D3, x = "label", y = "weight",
          color = "label",
          palette = "jco",
          add = "jitter")

Boxplot 2: nocardio

There are dots outside the boxplot. The dots are not close to the whiskers. The dots are not far away from the whiskers. Based on these findings, the boxplot is normal.

Boxplot 2: cardio

There are dots outside the boxplot. The dots are not close to the whiskers. The dots are not far away from the whiskers. Based on these findings, the boxplot is normal.

shapiro walk test

shapiro.test(D3$weight[D3$label == "nocardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  D3$weight[D3$label == "nocardio"]
## W = 0.96745, p-value = 0.5812

shapiro.test(D3$weight[D3$label == "cardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  D3$weight[D3$label == "cardio"]
## W = 0.97686, p-value = 0.8166

1: nocardio

The first group is normally distributed, (p > 0.05).

##Group 2: cardio The second group is normally distributed, (p > 0.05).

independent t test

t.test(weight ~ label, data = D3, var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  weight by label
## 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:
##  -8.1605622  0.3280454
## sample estimates:
##   mean in group cardio mean in group nocardio 
##               70.81710               74.73336

#An Independent T-Test was conducted to determine if there was a difference in weight in kg between nocardio and cardio groups. #nocardio scores (M = 74.7, SD = 7.57) were [not significantly] different from Group2 scores (M = 70.8, SD = 7.35), t(48) = -1.855, p > 0.05 .