creaing a table showing the average salary of males and females, who were placed. Reviewing whether there is a gender gap in the data. In other words, observing whether the average salaries of males is higher than the average salaries of females in this dataset.
## Parsed with column specification:
## cols(
## .default = col_integer(),
## Gender = col_character(),
## Percent_SSC = col_double(),
## Board_SSC = col_character(),
## Percent_HSC = col_double(),
## Board_HSC = col_character(),
## Stream_HSC = col_character(),
## Percent_Degree = col_double(),
## Course_Degree = col_character(),
## Entrance_Test = col_character(),
## Percentile_ET = col_double(),
## `S-TEST*SCORE` = col_double(),
## Percent_MBA = col_double(),
## Specialization_MBA = col_character(),
## Placement = col_character()
## )## See spec(...) for full column specifications.## Gender Salary
## 1 F 253068.0
## 2 M 284241.94.Hypothesis:“The average salary of the male MBAs is higher than the average salary of female MBAs.” H0= there is no significant difference between the average salary of male and female MBA’s H1= there is significant difference between the average salary of male and female MBA’s
##
## 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 284241.95.The p value=0.00234 6.Since the p value 0.00234 is lesser than 0.05 we rejectnull hypothesis therefore there is significant difference between the average salary of male and female MBA’s who were placed.ie)The average salary of the male MBAs is higher than the average salary of female MBAs.
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.