MBA Salary
Abhishek Rana
20 February 2018
setwd("C:/Users/Abhi/Desktop/Data Analytics/Week 4 Day 2")
salary.df <- read.csv(paste("MBA Starting Salaries Data.csv",sep=""))
head(salary.df)## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter work_yrs
## 1 23 2 620 77 87 87 3.4 3.00 1 2
## 2 24 1 610 90 71 87 3.5 4.00 1 2
## 3 24 1 670 99 78 95 3.3 3.25 1 2
## 4 24 1 570 56 81 75 3.3 2.67 1 1
## 5 24 2 710 93 98 98 3.6 3.75 1 2
## 6 24 1 640 82 89 91 3.9 3.75 1 2
## frstlang salary satis
## 1 1 0 7
## 2 1 0 6
## 3 1 0 6
## 4 1 0 7
## 5 1 999 5
## 6 1 0 6summary(salary.df)## 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 gmat_tpc s_avg f_avg
## Min. :16.00 Min. : 0.0 Min. :2.000 Min. :0.000
## 1st Qu.:71.00 1st Qu.:78.0 1st Qu.:2.708 1st Qu.:2.750
## Median :81.00 Median :87.0 Median :3.000 Median :3.000
## Mean :78.32 Mean :84.2 Mean :3.025 Mean :3.062
## 3rd Qu.:91.00 3rd Qu.:94.0 3rd Qu.:3.300 3rd Qu.:3.250
## Max. :99.00 Max. :99.0 Max. :4.000 Max. :4.000
## quarter work_yrs frstlang salary
## Min. :1.000 Min. : 0.000 Min. :1.000 Min. : 0
## 1st Qu.:1.250 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.: 0
## Median :2.000 Median : 3.000 Median :1.000 Median : 999
## Mean :2.478 Mean : 3.872 Mean :1.117 Mean : 39026
## 3rd Qu.:3.000 3rd Qu.: 4.000 3rd Qu.:1.000 3rd Qu.: 97000
## Max. :4.000 Max. :22.000 Max. :2.000 Max. :220000
## satis
## Min. : 1.0
## 1st Qu.: 5.0
## Median : 6.0
## Mean :172.2
## 3rd Qu.: 7.0
## Max. :998.0str(salary.df)## 'data.frame': 274 obs. of 13 variables:
## $ age : int 23 24 24 24 24 24 25 25 25 25 ...
## $ sex : int 2 1 1 1 2 1 1 2 1 1 ...
## $ gmat_tot: int 620 610 670 570 710 640 610 650 630 680 ...
## $ gmat_qpc: int 77 90 99 56 93 82 89 88 79 99 ...
## $ gmat_vpc: int 87 71 78 81 98 89 74 89 91 81 ...
## $ gmat_tpc: int 87 87 95 75 98 91 87 92 89 96 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.6 3.9 3.4 3.3 3.3 3.45 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.75 3.5 3.75 3.25 3.67 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 2 2 2 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 999 0 0 0 999 998 ...
## $ satis : int 7 6 6 7 5 6 5 6 4 998 ...boxplot(salary.df$age,main="Box Plot of Age of Students",col="blue",horizontal = TRUE,xlab="Age")
boxplot(salary.df$gmat_tot,main="Box Plot of GMAT Score of Students", xlab="Total GMAT score", horizontal = TRUE, col = "blue")
boxplot(salary.df$work_yrs,main="Box Plot of Total Work Experience of students", xlab="Years of work experience", horizontal = TRUE, col = "blue")
library("lattice")
attach(salary.df)
histogram( ~ salary | sex, data = salary.df, col="Red")
f1<- salary.df[ which(salary.df$satis<='7'), ]
hist(f1$satis,breaks =5,col="Yellow",xlab="Degree of Satisfaction (1=low,7=high)", main="Satisfaction of MBA students")
library(car)
scatterplot(salary ~ age,data=salary.df,
main="Scatter plot of salary vs age",
xlab="age",
ylab="salary")
library(car)
scatterplot(salary ~gmat_tot,data=salary.df,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter plot of salary vs Gmat total",
xlab="Gmat score",
ylab="salary")
library(corrgram)
corrgram(salary.df,lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of salary intercorrelations")
fresher <- subset(salary.df,salary!=0 & salary!=998 & salary!=999)
fresher <- xtabs(~salary+sex+age,fresher)
fresher## , , age = 22
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 1
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
## 95000 0 0
## 96000 0 0
## 96500 0 0
## 97000 0 0
## 98000 0 0
## 99000 0 0
## 100000 0 0
## 100400 0 0
## 101000 0 0
## 101100 0 0
## 101600 0 0
## 102500 0 0
## 103000 0 0
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## 105000 0 0
## 106000 0 0
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## 118000 0 0
## 120000 0 0
## 126710 0 0
## 130000 0 0
## 145800 0 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 23
##
## sex
## salary 1 2
## 64000 0 0
## 77000 1 0
## 78256 0 1
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
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## 96500 0 0
## 97000 0 0
## 98000 1 0
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## 100000 0 1
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## 101100 0 0
## 101600 0 0
## 102500 0 0
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## 126710 0 0
## 130000 0 0
## 145800 0 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 24
##
## sex
## salary 1 2
## 64000 0 1
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
## 95000 1 0
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## 97000 0 0
## 98000 2 1
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## 100000 2 2
## 100400 0 0
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## 102500 0 0
## 103000 0 0
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## 120000 0 0
## 126710 0 0
## 130000 0 0
## 145800 1 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 25
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 1
## 86000 0 1
## 88000 0 1
## 88500 0 0
## 90000 2 0
## 92000 1 1
## 93000 1 0
## 95000 3 2
## 96000 1 0
## 96500 0 0
## 97000 0 0
## 98000 0 2
## 99000 0 0
## 100000 0 1
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## 102500 0 0
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## 120000 0 0
## 126710 0 0
## 130000 0 0
## 145800 0 0
## 146000 0 0
## 162000 1 0
## 220000 0 0
##
## , , age = 26
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 1
## 85000 1 0
## 86000 0 1
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
## 95000 0 0
## 96000 1 1
## 96500 0 0
## 97000 0 0
## 98000 0 1
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## 100000 0 1
## 100400 0 0
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## 130000 1 0
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## 220000 0 0
##
## , , age = 27
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 1
## 86000 0 0
## 88000 0 0
## 88500 1 0
## 90000 1 0
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## 100000 1 0
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## 120000 1 0
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## 162000 0 0
## 220000 0 0
##
## , , age = 28
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
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## 93000 0 1
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## 120000 1 0
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## 220000 0 0
##
## , , age = 29
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
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##
## , , age = 30
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
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## 110000 0 0
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## 115000 0 0
## 118000 0 0
## 120000 1 1
## 126710 0 0
## 130000 0 0
## 145800 0 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 31
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 1 0
## 95000 0 0
## 96000 0 0
## 96500 0 0
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## 100000 1 0
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##
## , , age = 32
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
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## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 33
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
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## 100000 0 0
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## 118000 1 0
## 120000 0 0
## 126710 0 0
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## 145800 0 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 34
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
## 93000 0 0
## 95000 0 0
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## 100000 0 0
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## 102500 0 0
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## 118000 0 0
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## 220000 0 0
##
## , , age = 39
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
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## 100000 0 0
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## 102500 0 0
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## 110000 0 0
## 112000 1 0
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## 118000 0 0
## 120000 0 0
## 126710 0 0
## 130000 0 0
## 145800 0 0
## 146000 0 0
## 162000 0 0
## 220000 0 0
##
## , , age = 40
##
## sex
## salary 1 2
## 64000 0 0
## 77000 0 0
## 78256 0 0
## 82000 0 0
## 85000 0 0
## 86000 0 0
## 88000 0 0
## 88500 0 0
## 90000 0 0
## 92000 0 0
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## 110000 0 0
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## 120000 0 0
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## 130000 0 0
## 145800 0 0
## 146000 1 0
## 162000 0 0
## 220000 0 1mytable2 <- xtabs(~salary+age,data=fresher)
mytable2## age
## salary 22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
## 64000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 77000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 78256 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 82000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 85000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 86000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 88000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 88500 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 90000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 92000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 93000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 95000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 96000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 96500 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 97000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 98000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 99000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 100000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 100400 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 101000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 101100 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 101600 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 102500 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 103000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 104000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 105000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 106000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 107000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 107300 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 107500 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 108000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 110000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 112000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 115000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 118000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 120000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 126710 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 130000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 145800 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 146000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 162000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 220000 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2chisq.test(mytable2)## Warning in chisq.test(mytable2): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable2
## X-squared = 0, df = 574, p-value = 1Regression Analysis
ra<-lm(salary~quarter+s_avg+f_avg+age+gmat_tot+gmat_tpc+gmat_vpc+gmat_qpc,salary.df)
summary(ra)##
## Call:
## lm(formula = salary ~ quarter + s_avg + f_avg + age + gmat_tot +
## gmat_tpc + gmat_vpc + gmat_qpc, data = salary.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64369 -38647 -28385 51442 207646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 167402.3 76080.9 2.200 0.0286 *
## quarter -5693.9 4268.8 -1.334 0.1834
## s_avg 15122.7 13684.2 1.105 0.2701
## f_avg -7715.9 7059.7 -1.093 0.2754
## age -1216.9 863.8 -1.409 0.1601
## gmat_tot -331.7 221.1 -1.500 0.1347
## gmat_tpc 453.9 439.1 1.034 0.3022
## gmat_vpc 392.7 559.4 0.702 0.4833
## gmat_qpc 414.4 612.4 0.677 0.4992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50270 on 265 degrees of freedom
## Multiple R-squared: 0.05518, Adjusted R-squared: 0.02665
## F-statistic: 1.934 on 8 and 265 DF, p-value: 0.0552ra2<-lm(salary~sex+frstlang+satis+work_yrs+quarter,salary.df)
summary(ra2)##
## Call:
## lm(formula = salary ~ sex + frstlang + satis + work_yrs + quarter,
## data = salary.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62122 -43403 -4881 46588 195698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71445.525 15586.033 4.584 7.00e-06 ***
## sex 3398.218 6728.843 0.505 0.61396
## frstlang -7186.851 9025.303 -0.796 0.42656
## satis -45.884 7.834 -5.857 1.37e-08 ***
## work_yrs -665.349 900.024 -0.739 0.46040
## quarter -7327.600 2641.650 -2.774 0.00593 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47580 on 268 degrees of freedom
## Multiple R-squared: 0.144, Adjusted R-squared: 0.128
## F-statistic: 9.014 on 5 and 268 DF, p-value: 6.211e-08Unplaced students.
unplaced <- subset(salary.df,salary==0)
summary(unplaced)## 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.:570.0 1st Qu.:68.25
## Median :27.00 Median :1.000 Median :610.0 Median :82.00
## Mean :28.51 Mean :1.256 Mean :614.3 Mean :78.91
## 3rd Qu.:29.75 3rd Qu.:1.750 3rd Qu.:650.0 3rd Qu.:93.00
## Max. :48.00 Max. :2.000 Max. :760.0 Max. :99.00
## gmat_vpc gmat_tpc s_avg f_avg
## Min. :22.00 Min. : 0.00 Min. :2.000 Min. :0.000
## 1st Qu.:70.25 1st Qu.:73.50 1st Qu.:2.800 1st Qu.:2.750
## Median :81.00 Median :86.00 Median :3.000 Median :3.000
## Mean :77.63 Mean :82.29 Mean :3.031 Mean :3.062
## 3rd Qu.:89.00 3rd Qu.:93.00 3rd Qu.:3.300 3rd Qu.:3.250
## Max. :99.00 Max. :99.00 Max. :3.900 Max. :4.000
## quarter work_yrs frstlang salary
## Min. :1.000 Min. : 0.000 Min. :1.000 Min. :0
## 1st Qu.:2.000 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.:0
## Median :2.500 Median : 3.000 Median :1.000 Median :0
## Mean :2.544 Mean : 4.589 Mean :1.089 Mean :0
## 3rd Qu.:3.000 3rd Qu.: 5.000 3rd Qu.:1.000 3rd Qu.:0
## Max. :4.000 Max. :22.000 Max. :2.000 Max. :0
## satis
## Min. :4.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.622
## 3rd Qu.:6.000
## Max. :7.000attach(unplaced)## The following objects are masked from salary.df:
##
## age, f_avg, frstlang, gmat_qpc, gmat_tot, gmat_tpc, gmat_vpc,
## quarter, s_avg, salary, satis, sex, work_yrschisq.test(work_yrs,satis)## Warning in chisq.test(work_yrs, satis): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: work_yrs and satis
## X-squared = 44.974, df = 48, p-value = 0.5976