MBA Starting Salaries Analysis
SAI DHEERAJ VUJJINI
February 20, 2018
Reading data into R
mba.df<-read.csv(paste("MBA Starting Salaries Data.csv",sep=""))
View(mba.df)
mba1.df<-mba.df[ which(mba.df$satis!=998 & mba.df$salary!=999),]
mba2.df<-mba.df[ which(mba.df$salary!=998 & mba.df$salary!=999 & mba.df$salary!=0), ]Summary statistics for the important variables in the dataset.
summary(mba.df)[,c(1:5,9:12)]## age sex gmat_tot gmat_qpc
## Min. :22.00 Min. :1.000 Min. :450.0 Min. :28.00
## 1st Qu.:25.00 1st Qu.:1.000 1st Qu.:580.0 1st Qu.:72.00
## Median :27.00 Median :1.000 Median :620.0 Median :83.00
## Mean :27.36 Mean :1.248 Mean :619.5 Mean :80.64
## 3rd Qu.:29.00 3rd Qu.:1.000 3rd Qu.:660.0 3rd Qu.:93.00
## Max. :48.00 Max. :2.000 Max. :790.0 Max. :99.00
## gmat_vpc quarter work_yrs frstlang
## Min. :16.00 Min. :1.000 Min. : 0.000 Min. :1.000
## 1st Qu.:71.00 1st Qu.:1.250 1st Qu.: 2.000 1st Qu.:1.000
## Median :81.00 Median :2.000 Median : 3.000 Median :1.000
## Mean :78.32 Mean :2.478 Mean : 3.872 Mean :1.117
## 3rd Qu.:91.00 3rd Qu.:3.000 3rd Qu.: 4.000 3rd Qu.:1.000
## Max. :99.00 Max. :4.000 Max. :22.000 Max. :2.000
## salary
## Min. : 0
## 1st Qu.: 0
## Median : 999
## Mean : 39026
## 3rd Qu.: 97000
## Max. :220000library(psych)
describe(mba.df)[,c(1:5,9:12)]## vars n mean sd median max range skew kurtosis
## age 1 274 27.36 3.71 27 48 26 2.16 6.45
## sex 2 274 1.25 0.43 1 2 1 1.16 -0.66
## gmat_tot 3 274 619.45 57.54 620 790 340 -0.01 0.06
## gmat_qpc 4 274 80.64 14.87 83 99 71 -0.92 0.30
## gmat_vpc 5 274 78.32 16.86 81 99 83 -1.04 0.74
## gmat_tpc 6 274 84.20 14.02 87 99 99 -2.28 9.02
## s_avg 7 274 3.03 0.38 3 4 2 -0.06 -0.38
## f_avg 8 274 3.06 0.53 3 4 4 -2.08 10.85
## quarter 9 274 2.48 1.11 2 4 3 0.02 -1.35
## work_yrs 10 274 3.87 3.23 3 22 22 2.78 9.80
## frstlang 11 274 1.12 0.32 1 2 1 2.37 3.65
## salary 12 274 39025.69 50951.56 999 220000 220000 0.70 -1.05
## satis 13 274 172.18 371.61 6 998 997 1.77 1.13Box Plots / Bar Plots to visualize the distribution of each variable independently.
hist(mba.df$age,
main="Age distribution",
xlab="age",
ylab="Frequencies", col="grey" )
mba.df$sex=factor(mba.df$sex, levels=c(1,2), labels=c("Male","Female"))
plot(mba.df$sex,
main="Gender distribution",
col="green" )
hist(mba.df$gmat_tot,
main="Gmat score distribution",
xlab="scores",
ylab="Frequencies", col="grey", breaks = 30 )
mba.df$frstlang=factor(mba.df$frstlang, levels=c(1,2), labels=c("English","other"))
plot(mba.df$frstlang,
main="language distribution",
col="grey" )
hist(mba.df$work_yrs,
main="work experience distribution",
xlab="experience years",
ylab="Frequencies", col="grey", breaks = 30 )
rating <- mba.df[ which(mba.df$satis<='7'), ]
hist(rating$satis,
main="Satisfaction distribution",
xlab="Rating (1=low,7=high)",
ylab="Frequencies", col="grey",)
salary.df <- mba.df[ which(mba.df$salary !="998" & mba.df$salary !="999"), ]
hist(salary.df$salary,
main="Salary distribution",
xlab="Salary",
ylab="Frequencies", col="grey",)
Salary comparison to other variables
Salary and sex
library(lattice)
boxplot(salary~sex, data=mba2.df,ylab="1 = Male; 2 = Female",xlab="Salary", horizontal=TRUE )
Salary and age
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(mba2.df$age, mba2.df$salary,
xlab="age", ylab="salary", main = "salary vs age")
Salary and work-experience
scatterplot(mba1.df$work_yrs, mba1.df$salary, xlab="Work experience (years) of those who got jobs", ylab="Salary", main="Salary vs Work Experience")
Salary and language
boxplot(mba2.df$frstlang, mba2.df$salary, xlab="1 = English Speaking 2 = others",ylab="Salary",main="Salary & language")
Corrgram
library(corrgram)
corrgram(mba2.df, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, diag.panel=panel.minmax, text.panel=panel.txt, main="Corrgram of mba starting salaries")
TASK 1b: WHO GOT HOW MUCH SALARY?
Tables
Salary & Sex
table(mba1.df$salary, mba1.df$sex)##
## 1 2
## 0 67 23
## 64000 0 1
## 77000 1 0
## 78256 0 1
## 82000 0 1
## 85000 1 3
## 86000 0 2
## 88000 0 1
## 88500 1 0
## 90000 3 0
## 92000 2 1
## 93000 2 1
## 95000 4 3
## 96000 3 1
## 96500 1 0
## 97000 2 0
## 98000 6 4
## 99000 0 1
## 100000 4 5
## 100400 1 0
## 101000 0 2
## 101100 1 0
## 101600 1 0
## 102500 1 0
## 103000 1 0
## 104000 2 0
## 105000 11 0
## 106000 2 1
## 107000 1 0
## 107300 1 0
## 107500 1 0
## 108000 2 0
## 110000 0 1
## 112000 3 0
## 115000 5 0
## 118000 1 0
## 120000 3 1
## 126710 1 0
## 130000 1 0
## 145800 1 0
## 146000 1 0
## 162000 1 0
## 220000 0 1Salary & age
table(mba1.df$salary, mba1.df$age)##
## 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 39 40 42 43 48
## 0 1 3 13 9 10 14 6 11 2 2 5 0 3 3 2 1 1 0 1 2 1
## 64000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 77000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 78256 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85000 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 86000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 90000 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 92000 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93000 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 95000 0 0 1 5 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96000 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98000 0 1 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 99000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 100000 0 1 4 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 100400 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101000 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101100 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101600 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 102500 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 104000 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 105000 0 1 1 2 3 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0
## 106000 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0
## 107000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 107300 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 107500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 108000 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112000 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0
## 115000 0 0 1 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 120000 0 0 0 0 0 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
## 126710 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145800 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 162000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0salary & gmat score
table(mba1.df$salary, mba1.df$gmat_tot)##
## 450 480 500 510 520 530 540 550 560 570 580 590 600 610 620 630
## 0 1 1 0 2 0 3 3 4 8 7 4 3 3 9 4 5
## 64000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 77000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 78256 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 82000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 86000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 88000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 90000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
## 92000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 93000 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0
## 95000 0 0 0 0 0 1 0 0 2 0 0 0 0 2 0 0
## 96000 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0
## 96500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0
## 98000 0 0 0 0 0 0 0 0 1 3 1 1 0 1 0 0
## 99000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 100000 0 0 0 0 0 0 0 0 2 0 1 0 1 1 0 1
## 100400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 101000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## 101100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 102500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 104000 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
## 105000 0 0 0 0 0 0 0 2 0 2 3 0 1 0 1 0
## 106000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 107000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 107300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 107500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 108000 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## 110000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 115000 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1
## 118000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 120000 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0
## 126710 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145800 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 162000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 640 650 660 670 680 700 710 720 730 740 750 760
## 0 6 5 3 4 3 2 4 2 1 1 1 1
## 64000 0 0 0 0 0 0 0 0 0 0 0 0
## 77000 0 0 1 0 0 0 0 0 0 0 0 0
## 78256 0 0 0 0 0 0 0 0 0 0 0 0
## 82000 0 0 0 1 0 0 0 0 0 0 0 0
## 85000 0 0 1 0 0 1 0 1 0 0 0 0
## 86000 0 0 0 0 1 0 0 0 0 0 0 0
## 88000 0 1 0 0 0 0 0 0 0 0 0 0
## 88500 0 0 0 0 0 0 0 0 0 0 0 0
## 90000 0 1 0 0 0 0 0 0 0 0 0 0
## 92000 0 0 1 0 0 0 1 0 0 0 0 0
## 93000 0 0 0 0 0 0 0 0 0 0 0 0
## 95000 0 0 0 2 0 0 0 0 0 0 0 0
## 96000 0 1 0 0 0 0 0 0 0 0 0 0
## 96500 0 0 0 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 0 0 0 0 0 0 0 0 0
## 98000 0 0 0 1 1 0 1 0 0 0 0 0
## 99000 0 0 0 0 0 0 0 0 0 0 0 0
## 100000 0 2 0 0 0 0 1 0 0 0 0 0
## 100400 0 0 0 0 0 0 0 0 0 0 0 0
## 101000 0 0 0 0 0 0 0 0 0 0 0 0
## 101100 0 0 1 0 0 0 0 0 0 0 0 0
## 101600 0 0 0 0 0 0 0 0 0 0 0 0
## 102500 0 0 0 1 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 0 0 0 0 0 0 0
## 104000 0 0 0 0 0 0 0 0 0 0 0 0
## 105000 0 1 0 0 1 0 0 0 0 0 0 0
## 106000 0 0 0 0 2 0 0 0 0 0 0 0
## 107000 0 0 0 0 0 0 0 0 0 0 0 0
## 107300 0 0 1 0 0 0 0 0 0 0 0 0
## 107500 0 0 0 0 0 0 0 0 0 0 0 0
## 108000 0 0 0 0 0 0 0 0 0 0 0 0
## 110000 1 0 0 0 0 0 0 0 0 0 0 0
## 112000 0 0 0 1 1 0 0 0 0 0 0 0
## 115000 0 0 0 0 0 0 1 0 0 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 0 0 0
## 120000 0 0 0 1 0 1 0 0 0 0 0 0
## 126710 0 0 0 0 0 0 0 0 0 0 0 0
## 130000 0 1 0 0 0 0 0 0 0 0 0 0
## 145800 0 0 0 0 0 0 0 0 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0
## 162000 0 0 0 0 0 1 0 0 0 0 0 0
## 220000 0 0 0 0 0 0 0 0 0 0 0 0Salary & Language
table(mba1.df$salary, mba1.df$frstlang)##
## 1 2
## 0 82 8
## 64000 1 0
## 77000 1 0
## 78256 1 0
## 82000 1 0
## 85000 4 0
## 86000 2 0
## 88000 1 0
## 88500 1 0
## 90000 3 0
## 92000 3 0
## 93000 3 0
## 95000 7 0
## 96000 4 0
## 96500 1 0
## 97000 2 0
## 98000 8 2
## 99000 0 1
## 100000 9 0
## 100400 1 0
## 101000 2 0
## 101100 1 0
## 101600 1 0
## 102500 1 0
## 103000 1 0
## 104000 1 1
## 105000 11 0
## 106000 3 0
## 107000 1 0
## 107300 0 1
## 107500 1 0
## 108000 2 0
## 110000 1 0
## 112000 3 0
## 115000 5 0
## 118000 0 1
## 120000 4 0
## 126710 1 0
## 130000 1 0
## 145800 1 0
## 146000 1 0
## 162000 1 0
## 220000 0 1Salary and Work-experience
table(mba1.df$salary, mba1.df$work_yrs)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 18 22
## 0 1 12 22 14 9 12 2 5 2 1 1 2 2 1 0 1 1 2
## 64000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 77000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 78256 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85000 0 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 86000 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88500 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 90000 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 92000 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93000 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0
## 95000 1 1 2 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 96000 0 1 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96500 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98000 0 0 7 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0
## 99000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 100000 0 0 6 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## 100400 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101000 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101100 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 101600 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 102500 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 104000 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 105000 0 0 4 4 0 1 1 0 0 0 0 0 0 0 0 1 0 0
## 106000 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0
## 107000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 107300 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 107500 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 108000 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 112000 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
## 115000 0 2 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 120000 0 0 0 1 0 2 0 0 1 0 0 0 0 0 0 0 0 0
## 126710 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145800 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 162000 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0Chi Square Test
Evaluating whether the male students recieve higher salary than that of female students.
task.a<-xtabs(~ sex + salary, data=mba1.df)
chisq.test(task.a)## Warning in chisq.test(task.a): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: task.a
## X-squared = 55.494, df = 42, p-value = 0.07929As p-value is greater than 0.05 we conclude that there is no difference between the salaries of males and females.
Evaluating whether those having English as first language recieve higher salary.
task.b<-xtabs(~ frstlang + salary, data=mba1.df)
chisq.test(task.b)## Warning in chisq.test(task.b): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: task.b
## X-squared = 62.016, df = 42, p-value = 0.02384Regression Models
mba1.df$frstlang <- as.factor(mba1.df$frstlang)
mba1.df$sex <- as.factor(mba1.df$sex)
str(mba1.df)## 'data.frame': 193 obs. of 13 variables:
## $ age : int 23 24 24 24 24 25 25 27 27 28 ...
## $ sex : Factor w/ 2 levels "1","2": 2 1 1 1 1 1 2 1 1 2 ...
## $ gmat_tot: int 620 610 670 570 640 610 650 740 750 540 ...
## $ gmat_qpc: int 77 90 99 56 82 89 88 99 99 75 ...
## $ gmat_vpc: int 87 71 78 81 89 74 89 96 98 50 ...
## $ gmat_tpc: int 87 87 95 75 91 87 92 99 99 65 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.9 3.4 3.3 3.5 3.4 3.6 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.5 3.75 3.5 3.5 4 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 3 1 5 ...
## $ frstlang: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 0 0 0 0 0 0 ...
## $ satis : int 7 6 6 7 6 5 6 6 5 5 ...m1 <- lm(salary~ sex, data=mba1.df)
summary(m1)##
## Call:
## lm(formula = salary ~ sex, data = mba1.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56560 -54373 28440 45627 163440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54374 4519 12.031 <2e-16 ***
## sex2 2187 8544 0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53280 on 191 degrees of freedom
## Multiple R-squared: 0.0003429, Adjusted R-squared: -0.004891
## F-statistic: 0.06551 on 1 and 191 DF, p-value: 0.7983m2 <- lm(salary~ frstlang, data=mba1.df)
summary(m2)##
## Call:
## lm(formula = salary ~ frstlang, data = mba1.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56287 -54876 30124 45124 163713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54876 3994 13.739 <2e-16 ***
## frstlang2 1411 14327 0.098 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53290 on 191 degrees of freedom
## Multiple R-squared: 5.078e-05, Adjusted R-squared: -0.005185
## F-statistic: 0.009699 on 1 and 191 DF, p-value: 0.9217TASK 2c: COMPARE THOSE WHO GOT A JOB WITH THOSE WHO DID NOT GET A JOB?
table
table(mba1.df$salary != "0", mba1.df$salary> "0")##
## FALSE TRUE
## FALSE 90 0
## TRUE 0 103Chi-square test.
task.c <- table(mba1.df$salary != "0", mba1.df$salary> "0")
chisq.test(task.c)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: task.c
## X-squared = 189, df = 1, p-value < 2.2e-16