library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(effsize)
library(rstatix)
##
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
##
## filter
A6Q1 <- read_excel("A6Q1.xlsx")
View(A6Q1)
Before <- A6Q1$Before
After <- A6Q1$After
Differences <- After - Before
mean(Before, na.rm = TRUE)
## [1] 76.13299
median(Before, na.rm = TRUE)
## [1] 75.95988
sd(Before, na.rm = TRUE)
## [1] 7.781323
mean(After, na.rm = TRUE)
## [1] 71.58994
median(After, na.rm = TRUE)
## [1] 70.88045
sd(After, na.rm = TRUE)
## [1] 6.639509
hist(Differences,
main = "Histogram of Difference in weight",
xlab = "Value",
ylab = "Frequency",
col = "pink",
border = "black",
breaks = 20)
Histogram of Difference in Weight
The difference in body weight look abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.
boxplot(Differences,
main = "Distribution of Weight Differences (After - Before)",
ylab = "Difference in Weight",
col = "blue",
border = "darkblue")
Boxplot
There is one dot outside the boxplot. The dot is close to the whiskers. The dot is not very far away from the whiskers. Based on these findings, the boxplot is normal.
shapiro.test(Differences)
##
## Shapiro-Wilk normality test
##
## data: Differences
## W = 0.94757, p-value = 0.3318
Shapiro-Wilk Difference in Weight
The data is normally distributed, (p = .332).
t.test(Before, After, paired = TRUE, na.action = na.omit)
##
## Paired t-test
##
## data: Before and After
## t = 1.902, df = 19, p-value = 0.07245
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.4563808 9.5424763
## sample estimates:
## mean difference
## 4.543048
A Dependent T-Test was conducted to determine if there was a difference in body weight before the diet versus after the diet.
Before diet (M = 76.13, SD = 7.78) was not significantly different from after diet (M = 71.59, SD = 6.64), t(19) = 1.90, p-value = 0.072.