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)
ds3 <- read_excel("~/Downloads/ds3.xlsx")
ds3 %>%
     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(ds3$Weight[ds3$Exercise == "nocardio"],
      main = "Histogram of nocardio Exercise",
      xlab = "Value",
      ylab = "Frequency",
      col = "lightblue",
      border = "black",
      breaks = 10)

 hist(ds3$Weight[ds3$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(ds3, x = "Exercise", y = "Weight",
           color = "Exercise",
           palette = "jco",
           add = "jitter")

Boxplot 1: nocardio There are dots outside the boxplot. The dots are past the whiskers of the boxplot. The dots are not very far away from the whiskers of the boxplot. Based on these findings, the boxplot is normal.

Boxplot 2: cardio There are dots outside the boxplot. The dots are past the whiskers of the boxplot. The dots are not very far away from the whiskers of the boxplot. Based on these findings, the boxplot is normal.

shapiro.test(ds3$Weight[ds3$Exercise == "nocardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  ds3$Weight[ds3$Exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166
shapiro.test(ds3$Weight[ds3$Exercise == "cardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  ds3$Weight[ds3$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 = ds3, 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 no cardio and cardio. No cardio scores (M = 70.8, SD = 7.35) were not significantly different from cardio scores (M = 74.7, SD = 7.57), t(48) = 1.86, p > .05.