setwd("C:/Users/jaya/downloads")
dean.df<-read.csv(paste("Data - Deans Dilemma.csv",sep=""))
#week 2 day1
# 3d
# 1. Submit your R code that creates a table showing the mean salary of males and females, who were placed.
placed.df<-dean.df[which(dean.df$Placement_B==1),]
aggregate(placed.df$Salary~placed.df$Gender, FUN = mean)
## placed.df$Gender placed.df$Salary
## 1 F 253068.0
## 2 M 284241.9
# 2. average salary of males is 284241.9
# 3. average salary of female is 284241.9
# 4. Submit R code to run a t-test for the Hypothesis "The average salary of the male MBAs is higher than the average salary of female MBAs."
t.test(placed.df$Salary~placed.df$Gender,data = placed.df)
##
## Welch Two Sample t-test
##
## data: placed.df$Salary by placed.df$Gender
## t = -3.0757, df = 243.03, p-value = 0.00234
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -51138.42 -11209.22
## sample estimates:
## mean in group F mean in group M
## 253068.0 284241.9
# 5. the p-value based on the t-test is 0.00234
# 6. Please interpret the meaning of the t-test, as applied to the average salaries of male and female MBAs.
# ans). Here the p-value < 0.05, so our null hypothesis is rejected.
# + Therefore, there's significant difference between the means of our sample population i.e. it is the average salary of the male MBAs is higher than the average salary of female MBAs.