mba_salary
Ashutosh Kumar
January 23, 2018
Reading data into R
mba=read.csv(paste("MBA Starting Salaries Data.csv",sep=""),)
View(mba)Summary
summary(mba)## 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. :998.0boxplot
boxplot(mba)
pairs(mba[,c(3:6,10:13)])
library("knitr")
library("corrgram")
corrgram(mba, order=TRUE,main="Corrgram of Mba Salaries",
lower.panel=panel.shade, upper.panel=panel.pie,
text.panel=panel.txt)
Creating variance covariance matrix
cov(mba)## age sex gmat_tot gmat_qpc
## age 1.376904e+01 -4.513248e-02 -3.115879e+01 -1.192655e+01
## sex -4.513248e-02 1.872677e-01 -1.328841e+00 -1.053769e+00
## gmat_tot -3.115879e+01 -1.328841e+00 3.310688e+03 6.200233e+02
## gmat_qpc -1.192655e+01 -1.053769e+00 6.200233e+02 2.210731e+02
## gmat_vpc -2.763643e+00 5.463758e-01 7.260006e+02 3.814826e+01
## gmat_tpc -8.839978e+00 -4.908960e-02 6.839911e+02 1.357997e+02
## s_avg 2.116874e-01 2.096227e-02 2.480257e+00 -1.691233e-01
## f_avg -3.399348e-02 2.082698e-02 3.154688e+00 5.753854e-01
## quarter -2.045935e-01 -6.414267e-02 -5.891153e+00 6.001979e-01
## work_yrs 1.029494e+01 -1.580172e-02 -3.391634e+01 -1.137186e+01
## frstlang 6.796610e-02 2.138980e-04 -2.499933e+00 6.646346e-01
## salary -1.183042e+04 1.518264e+03 -1.611600e+05 -3.335823e+04
## satis -1.763499e+02 -8.780808e+00 1.765263e+03 3.348371e+02
## gmat_vpc gmat_tpc s_avg f_avg
## age -2.7636427 -8.8399775 0.21168739 -0.03399348
## sex 0.5463758 -0.0490896 0.02096227 0.02082698
## gmat_tot 726.0006417 683.9910698 2.48025721 3.15468838
## gmat_qpc 38.1482581 135.7996845 -0.16912329 0.57538542
## gmat_vpc 284.2481217 157.4932488 1.31357023 0.67207000
## gmat_tpc 157.4932488 196.6057057 0.62710008 0.58698618
## s_avg 1.3135702 0.6271001 0.14521760 0.11016898
## f_avg 0.6720700 0.5869862 0.11016898 0.27567237
## quarter -3.2676666 -1.2923719 -0.32237213 -0.26080880
## work_yrs -3.6181653 -7.8575172 0.15926392 -0.06628700
## frstlang -2.1145691 -0.4663244 -0.01671372 -0.00626026
## salary -5273.8523836 3522.7500067 2831.60098580 787.65597177
## satis 392.3562739 484.2466779 -4.62884495 2.12532927
## quarter work_yrs frstlang salary
## age -2.045935e-01 10.29493864 6.796610e-02 -1.183042e+04
## sex -6.414267e-02 -0.01580172 2.138980e-04 1.518264e+03
## gmat_tot -5.891153e+00 -33.91633914 -2.499933e+00 -1.611600e+05
## gmat_qpc 6.001979e-01 -11.37186171 6.646346e-01 -3.335823e+04
## gmat_vpc -3.267667e+00 -3.61816529 -2.114569e+00 -5.273852e+03
## gmat_tpc -1.292372e+00 -7.85751718 -4.663244e-01 3.522750e+03
## s_avg -3.223721e-01 0.15926392 -1.671372e-02 2.831601e+03
## f_avg -2.608088e-01 -0.06628700 -6.260260e-03 7.876560e+02
## quarter 1.232119e+00 -0.30866822 3.553381e-02 -9.296214e+03
## work_yrs -3.086682e-01 10.44882490 -2.898318e-02 1.486147e+03
## frstlang 3.553381e-02 -0.02898318 1.035266e-01 -1.419586e+03
## salary -9.296214e+03 1486.14704152 -1.419586e+03 2.596062e+09
## satis -5.227133e-03 -131.24080907 9.484532e+00 -6.347115e+06
## satis
## age -1.763499e+02
## sex -8.780808e+00
## gmat_tot 1.765263e+03
## gmat_qpc 3.348371e+02
## gmat_vpc 3.923563e+02
## gmat_tpc 4.842467e+02
## s_avg -4.628845e+00
## f_avg 2.125329e+00
## quarter -5.227133e-03
## work_yrs -1.312408e+02
## frstlang 9.484532e+00
## salary -6.347115e+06
## satis 1.380974e+05creating a dataset of the students who actually got the job
mba1=mba[which(mba$salary!=0),]
View(mba1)contigency
table(mba1$salary,mba1$satis)##
## 1 2 3 4 5 6 7 998
## 998 0 0 0 0 0 0 0 46
## 999 1 1 4 12 9 7 1 0
## 64000 0 0 0 0 0 0 1 0
## 77000 0 0 0 0 0 1 0 0
## 78256 0 0 0 0 1 0 0 0
## 82000 0 0 0 0 0 0 1 0
## 85000 0 0 0 0 1 3 0 0
## 86000 0 0 0 0 2 0 0 0
## 88000 0 0 0 0 0 0 1 0
## 88500 0 0 0 0 0 1 0 0
## 90000 0 0 0 0 2 0 1 0
## 92000 0 0 0 0 1 1 1 0
## 93000 0 0 0 0 1 2 0 0
## 95000 0 0 1 1 1 2 2 0
## 96000 0 0 0 0 1 1 2 0
## 96500 0 0 0 0 0 1 0 0
## 97000 0 0 0 0 0 1 1 0
## 98000 0 0 0 0 2 5 3 0
## 99000 0 0 0 0 0 1 0 0
## 100000 0 0 0 0 1 6 2 0
## 100400 0 0 0 0 0 0 1 0
## 101000 0 0 0 0 1 1 0 0
## 101100 0 0 0 0 0 1 0 0
## 101600 0 0 0 0 0 1 0 0
## 102500 0 0 0 0 1 0 0 0
## 103000 0 0 0 0 0 1 0 0
## 104000 0 0 0 0 1 1 0 0
## 105000 0 0 0 0 4 6 1 0
## 106000 0 0 0 0 0 2 1 0
## 107000 0 0 0 0 1 0 0 0
## 107300 0 0 0 0 0 0 1 0
## 107500 0 0 0 0 1 0 0 0
## 108000 0 0 0 0 0 2 0 0
## 110000 0 0 0 0 1 0 0 0
## 112000 0 0 0 0 0 2 1 0
## 115000 0 0 0 0 3 2 0 0
## 118000 0 0 0 0 0 0 1 0
## 120000 0 0 0 0 2 2 0 0
## 126710 0 0 0 0 0 1 0 0
## 130000 0 0 0 0 0 0 1 0
## 145800 0 0 0 0 0 1 0 0
## 146000 0 0 0 0 0 1 0 0
## 162000 0 0 0 0 1 0 0 0
## 220000 0 0 0 0 0 1 0 0table(mba1$s_avg,mba1$f_avg)##
## 0 2 2.25 2.33 2.5 2.67 2.75 2.8 2.83 3 3.17 3.2 3.25 3.33 3.4 3.5
## 2.2 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 2.3 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## 2.4 0 1 1 0 2 0 3 0 0 0 0 0 0 0 0 0
## 2.45 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 2.5 0 0 1 0 3 0 4 0 0 2 0 0 0 0 0 0
## 2.6 0 0 0 0 4 0 3 0 0 3 0 0 0 0 0 0
## 2.67 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.7 0 0 0 0 3 0 6 0 0 6 0 1 3 0 0 0
## 2.73 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2.8 0 0 0 0 0 0 3 0 0 5 0 0 2 0 0 0
## 2.9 0 0 0 0 0 0 5 1 0 6 0 0 6 1 0 1
## 2.91 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 4 0 0 6 0 0 4 0 0 0
## 3.09 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1
## 3.1 0 0 0 1 0 1 0 0 0 5 0 0 2 1 0 3
## 3.18 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3.2 0 0 0 0 0 0 0 0 0 4 0 0 6 0 1 1
## 3.27 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3.3 0 0 0 0 0 0 0 0 0 2 0 0 9 0 0 5
## 3.4 0 0 0 0 0 0 0 1 0 1 0 0 3 0 0 0
## 3.45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 3.5 0 0 0 0 0 1 0 0 0 2 0 0 3 0 0 4
## 3.56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
## 3.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 3.6 3.67 3.75 4
## 2.2 0 0 0 0
## 2.3 0 0 0 0
## 2.4 0 0 0 0
## 2.45 0 0 0 0
## 2.5 0 0 0 0
## 2.6 0 0 0 0
## 2.67 0 0 0 0
## 2.7 0 0 0 0
## 2.73 0 0 0 0
## 2.8 0 0 0 0
## 2.9 0 0 0 0
## 2.91 0 0 0 0
## 3 0 0 0 0
## 3.09 0 0 0 0
## 3.1 0 0 1 0
## 3.18 0 0 0 0
## 3.2 0 0 0 0
## 3.27 0 0 0 0
## 3.3 0 0 0 0
## 3.4 0 1 2 1
## 3.45 0 1 0 0
## 3.5 1 0 1 2
## 3.56 0 0 0 1
## 3.6 0 1 2 0
## 3.7 1 0 0 1
## 3.8 0 0 0 1
## 4 0 0 0 1table(mba1$gmat_tot,mba1$gmat_qpc)##
## 39 43 46 48 49 50 52 53 55 56 57 60 64 65 66 67 68 71 72 74 75 77 78
## 450 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 460 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 500 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
## 520 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 530 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 540 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 550 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0
## 560 1 0 0 0 0 0 3 0 1 0 1 1 1 0 0 0 1 0 0 0 1 0 0
## 570 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 1 0 0
## 580 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 0 1
## 590 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 1 0 0 0 0
## 600 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 4 0
## 610 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0
## 620 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1
## 630 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 0 0
## 640 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 650 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 660 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 670 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 680 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 690 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 710 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 720 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 730 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 740 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 790 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 79 81 82 83 84 85 87 88 89 90 91 92 93 94 95 96 97 98 99
## 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 460 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 520 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 530 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 540 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 550 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 560 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 570 0 0 2 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0
## 580 2 0 0 2 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
## 590 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
## 600 0 0 1 0 1 0 1 0 2 0 1 0 0 0 0 0 1 0 1
## 610 0 0 1 1 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0
## 620 1 1 1 0 1 1 1 1 2 0 0 0 1 0 0 0 2 0 0
## 630 3 0 1 2 1 1 2 0 0 0 0 0 2 0 0 1 0 0 0
## 640 2 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0
## 650 2 0 1 0 0 0 1 0 3 0 1 0 1 0 1 0 0 0 1
## 660 0 1 0 1 1 0 0 1 0 1 1 0 0 0 2 0 1 0 1
## 670 0 0 0 3 1 0 2 0 0 0 1 0 0 0 1 0 2 1 2
## 680 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 2 1 0 1
## 690 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1
## 700 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0
## 710 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 1 0 0 1
## 720 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 730 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 740 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
## 790 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1table(mba1$gmat_vpc,mba1$gmat_tpc)##
## 0 37 44 51 52 58 61 62 65 68 69 71 72 73 75 77 78 79 80 81 83 84 85
## 16 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
## 45 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0
## 54 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 0 0 2 0 2 0 2 0 0 1 0 1 1 0 0
## 62 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 2 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1
## 71 1 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 5 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0
## 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0
## 81 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 1 0 0 2 1 2 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0
## 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 86 87 88 89 90 91 92 93 94 95 96 97 98 99
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 46 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 54 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 1 0 0 0 0 1 0 0 0 0 0 0
## 67 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 71 1 1 0 1 0 1 0 0 0 1 0 0 0 0
## 74 0 5 0 0 0 0 0 0 0 0 1 0 0 0
## 75 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 81 2 1 0 0 1 1 0 1 1 2 2 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 84 1 1 0 2 0 1 0 3 1 0 0 0 0 0
## 85 0 0 0 1 0 0 0 0 0 0 2 0 0 0
## 87 1 0 1 4 1 0 0 1 0 0 1 2 0 0
## 89 0 1 0 1 1 0 0 1 0 1 0 1 0 0
## 90 0 0 0 0 0 0 0 0 0 1 0 1 0 0
## 91 0 0 0 2 0 0 0 2 0 0 0 0 0 0
## 92 0 0 0 0 0 0 1 0 1 0 0 0 0 1
## 93 1 0 0 1 0 1 0 1 2 1 0 1 1 0
## 95 0 0 0 2 0 1 0 1 1 2 0 1 3 0
## 96 0 1 0 0 0 0 0 0 0 1 2 0 1 0
## 97 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 98 1 1 0 0 0 0 1 0 0 1 2 0 4 5
## 99 0 0 0 0 0 0 0 0 0 1 1 0 0 2chisquare test
chisq.test(mba1$salary,mba1$satis)## Warning in chisq.test(mba1$salary, mba1$satis): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: mba1$salary and mba1$satis
## X-squared = 391.04, df = 301, p-value = 0.0003578chisq.test(mba1$s_avg,mba1$f_avg)## Warning in chisq.test(mba1$s_avg, mba1$f_avg): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: mba1$s_avg and mba1$f_avg
## X-squared = 1033.1, df = 494, p-value < 2.2e-16chisq.test(mba1$gmat_tot,mba1$gmat_qpc)## Warning in chisq.test(mba1$gmat_tot, mba1$gmat_qpc): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mba1$gmat_tot and mba1$gmat_qpc
## X-squared = 1559.3, df = 1066, p-value < 2.2e-16chisq.test(mba1$gmat_vpc,mba1$gmat_tpc)## Warning in chisq.test(mba1$gmat_vpc, mba1$gmat_tpc): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mba1$gmat_vpc and mba1$gmat_tpc
## X-squared = 1790.8, df = 1152, p-value < 2.2e-16t.test(mba1$s_avg,mba1$f_avg)##
## Welch Two Sample t-test
##
## data: mba1$s_avg and mba1$f_avg
## t = -0.81877, df = 339.4, p-value = 0.4135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13110158 0.05403637
## sample estimates:
## mean of x mean of y
## 3.022554 3.061087t.test(mba1$s_avg,mba1$f_avg)##
## Welch Two Sample t-test
##
## data: mba1$s_avg and mba1$f_avg
## t = -0.81877, df = 339.4, p-value = 0.4135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13110158 0.05403637
## sample estimates:
## mean of x mean of y
## 3.022554 3.061087t.test(mba1$s_avg,mba1$f_avg)##
## Welch Two Sample t-test
##
## data: mba1$s_avg and mba1$f_avg
## t = -0.81877, df = 339.4, p-value = 0.4135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13110158 0.05403637
## sample estimates:
## mean of x mean of y
## 3.022554 3.061087t.test(mba1$s_avg,mba1$f_avg)##
## Welch Two Sample t-test
##
## data: mba1$s_avg and mba1$f_avg
## t = -0.81877, df = 339.4, p-value = 0.4135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13110158 0.05403637
## sample estimates:
## mean of x mean of y
## 3.022554 3.061087regression model
y=B0+B1gmattot+B2gmat_qpc y=B0+B1gmat_vpc+B2gmat_tpc
summ=lm(mba1$salary~mba1$gmat_tot+mba1$gmat_qpc)
summ##
## Call:
## lm(formula = mba1$salary ~ mba1$gmat_tot + mba1$gmat_qpc)
##
## Coefficients:
## (Intercept) mba1$gmat_tot mba1$gmat_qpc
## 130669.17 -72.77 -334.92summary(summ)$r.squared## [1] 0.02226087summ1=lm(mba1$salary~mba1$gmat_vpc+mba1$gmat_tpc)
summ1##
## Call:
## lm(formula = mba1$salary ~ mba1$gmat_vpc + mba1$gmat_tpc)
##
## Coefficients:
## (Intercept) mba1$gmat_vpc mba1$gmat_tpc
## 83185.4 117.6 -403.2summary(summ1)$r.squared## [1] 0.006031796From the value of r square, model of regression gmat_total & gmat_qpc is better. Meaning score in Gmat influence starting salary better.
Other Subset
mba2=mba[which(mba$salary==0),]
View(mba2)contigency
table(mba2$salary,mba2$satis)##
## 4 5 6 7
## 0 4 36 40 10table(mba2$s_avg,mba2$f_avg)##
## 0 2 2.25 2.5 2.67 2.75 3 3.17 3.2 3.25 3.33 3.4 3.5 3.6 3.67 3.75
## 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2.2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2.4 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
## 2.6 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.7 0 0 0 2 0 3 2 0 0 1 0 0 0 0 0 0
## 2.8 0 0 0 1 0 2 4 0 0 2 0 0 0 0 0 0
## 2.82 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2.9 0 0 0 0 0 1 4 0 0 4 0 0 0 0 0 0
## 3 0 0 0 1 0 1 5 0 1 2 0 0 0 0 0 0
## 3.08 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 3.09 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
## 3.1 0 0 0 0 0 0 3 0 0 1 0 0 1 0 1 0
## 3.17 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3.2 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0
## 3.25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.27 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 3.3 0 0 0 0 1 1 1 0 0 3 0 0 0 0 0 2
## 3.38 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 3.4 0 0 0 0 0 0 2 0 0 1 0 0 3 0 0 1
## 3.45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3.6 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1
## 3.64 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 3.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##
## 3.83 4
## 2 0 0
## 2.1 0 0
## 2.2 0 0
## 2.3 0 0
## 2.4 0 0
## 2.6 0 0
## 2.7 0 0
## 2.8 0 0
## 2.82 0 0
## 2.9 0 0
## 3 0 0
## 3.08 0 0
## 3.09 0 0
## 3.1 0 0
## 3.17 0 0
## 3.2 0 0
## 3.25 0 0
## 3.27 0 0
## 3.3 0 1
## 3.38 0 0
## 3.4 0 0
## 3.45 1 0
## 3.5 0 1
## 3.6 0 1
## 3.64 0 0
## 3.8 0 1
## 3.9 0 0table(mba2$gmat_tot,mba2$gmat_qpc)##
## 28 35 43 48 49 52 56 57 59 60 61 64 66 68 69 72 73 74 75 77 79 81 82
## 450 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 480 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 510 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 530 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 540 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 550 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0
## 560 0 0 1 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0
## 570 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1
## 580 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0
## 590 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 610 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 2
## 620 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1
## 630 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2
## 640 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## 650 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 660 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 670 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 680 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 710 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 720 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 730 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 740 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 750 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 760 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 84 85 87 88 89 90 91 92 93 94 95 96 97 98 99
## 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 480 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 510 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 530 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 540 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 550 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 560 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 580 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 590 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 600 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
## 610 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0
## 620 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 630 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1
## 640 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0
## 650 0 0 1 2 1 0 0 0 0 0 0 0 0 0 0
## 660 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0
## 670 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1
## 680 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0
## 700 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## 710 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1
## 720 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 730 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 740 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 750 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 760 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1table(mba2$gmat_vpc,mba2$gmat_tpc)##
## 0 34 45 54 55 62 65 69 71 72 73 75 76 78 81 83 86 87 89 90 91 92 93
## 22 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 41 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 46 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 54 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 71 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0
## 74 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0
## 78 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 2 0 0 0 1 1 0
## 81 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 2 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
## 87 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 1 0
## 89 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1
## 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 94 95 96 97 98 99
## 22 0 0 0 0 0 0
## 41 0 0 0 0 0 0
## 45 0 0 0 0 0 0
## 46 0 0 0 0 0 0
## 50 0 0 0 0 0 0
## 54 0 0 0 0 0 0
## 58 0 0 0 0 0 0
## 62 0 0 0 0 0 0
## 63 0 0 0 0 0 0
## 67 0 0 0 0 0 0
## 70 0 0 0 0 0 0
## 71 0 0 0 0 0 0
## 74 0 0 0 0 0 0
## 78 0 1 0 0 0 0
## 81 0 0 0 0 0 0
## 84 1 0 0 0 0 0
## 87 1 1 1 0 1 0
## 89 0 0 1 0 0 0
## 90 0 0 0 0 0 0
## 91 0 2 0 0 1 0
## 92 0 0 0 1 0 0
## 95 0 0 0 0 0 1
## 96 0 0 1 0 0 2
## 97 0 0 0 0 0 2
## 98 0 0 0 0 1 3
## 99 0 0 0 0 0 1chi-square test
chisq.test(mba2$s_avg,mba2$f_avg)## Warning in chisq.test(mba2$s_avg, mba2$f_avg): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: mba2$s_avg and mba2$f_avg
## X-squared = 722.17, df = 442, p-value = 7.981e-16chisq.test(mba2$gmat_tot,mba2$gmat_qpc)## Warning in chisq.test(mba2$gmat_tot, mba2$gmat_qpc): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mba2$gmat_tot and mba2$gmat_qpc
## X-squared = 986.48, df = 925, p-value = 0.07862chisq.test(mba2$gmat_vpc,mba2$gmat_tpc)## Warning in chisq.test(mba2$gmat_vpc, mba2$gmat_tpc): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mba2$gmat_vpc and mba2$gmat_tpc
## X-squared = 856.17, df = 700, p-value = 4.502e-05