I did the analysis using R - below is the R program used

R program

[Workspace loaded from ~/.RData]

library(readxl)

Exe_8_6C <- read_excel(“C:/Users/myounes/Downloads/Exe 8.6C.xlsx”)

View(Exe_8_6C)

library(readxl)

library(data.table)

data.table 1.15.2 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com

Warning message:

package ‘data.table’ was built under R version 4.3.3

mydata = data.table(Exe_8_6C)

head(mydata)

Sex Income

1 M 40.6

2 M 54.6

3 M 38.6

4 M 58.2

5 M 34.6

6 M 42.9

sample1 = mydata[Sex==”M”]

sample2= mydata[Sex==”F”]

mean(sample1$Income) - mean(sample2$Income)

[1] 8.68

t.test(sample1$Income, sample2$Income, alternative = “two.sided”, var.equal = FALSE, conf.level = 0.95)

Welch Two Sample t-test

data: sample1$Income and sample2$Income

t = 3.2679, df = 116.8, p-value = 0.001423

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

3.419559 13.940441

sample estimates:

mean of x mean of y

52.91333 44.23333

Comments on the results

Since the p-value is very small (p < 0.05), we reject the null hypothesis. This provides strong evidence that the mean income for males is significantly higher than that of females.