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summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
You can also embed plots, for example:
#Question 1 : Submit your R code that creates a table showing
#the mean salary of males and females, who were placed.
dilema.df <- read.csv(paste("Data - Deans Dilemma.csv", sep=""))
placed.df <- dilema.df[which(dilema.df$Placement_B==1),]
aggregate(placed.df[, 26:26], list(placed.df$Gender), mean)
## Group.1 x
## 1 F 253068.0
## 2 M 284241.9
#Question 2 : What is the average salary of male MBAs who were placed? Ans. 284241.9
#Question 3 : What is the average salary of female MBAs who were placed? Ans. 253068.0
#Question 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(Salary~Gender,placed.df)
##
## Welch Two Sample t-test
##
## data: Salary by 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
#Question 5 : What is the p-value based on the t-test? Ans. p-value = 0.00234
#Question 6 : Please interpret the meaning of the t-test, as applied to the average salaries of male and female MBAs.
#The null hypothesis is rejected since the p-value < 0.05.Thus there is significance difference between the means of
#male MBA salaries and female MBA salaries and the average salary of male MBA's is higher than the average
#salary of female MBA's.
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