MBA Starting Salaries Analysis
Puneet Rajput
mba.df <- read.csv(paste("MBA Starting Salaries Data.csv",sep = ""))
mbasalary.df <- subset(mba.df, mba.df$salary!=998 & mba.df$salary!=999 & mba.df$satis!=998)
View(mbasalary.df)summary(mbasalary.df)## age sex gmat_tot gmat_qpc
## Min. :22.00 Min. :1.00 Min. :450.0 Min. :28.00
## 1st Qu.:25.00 1st Qu.:1.00 1st Qu.:570.0 1st Qu.:72.00
## Median :27.00 Median :1.00 Median :610.0 Median :82.00
## Mean :27.59 Mean :1.28 Mean :615.2 Mean :79.35
## 3rd Qu.:29.00 3rd Qu.:2.00 3rd Qu.:650.0 3rd Qu.:91.00
## Max. :48.00 Max. :2.00 Max. :760.0 Max. :99.00
## gmat_vpc gmat_tpc s_avg f_avg
## Min. :22.00 Min. : 0.00 Min. :2.000 Min. :0.000
## 1st Qu.:71.00 1st Qu.:75.00 1st Qu.:2.800 1st Qu.:2.750
## Median :81.00 Median :87.00 Median :3.090 Median :3.000
## Mean :78.13 Mean :83.48 Mean :3.064 Mean :3.078
## 3rd Qu.:91.00 3rd Qu.:93.00 3rd Qu.:3.300 3rd Qu.:3.330
## Max. :99.00 Max. :99.00 Max. :4.000 Max. :4.000
## quarter work_yrs frstlang salary
## Min. :1.000 Min. : 0.000 Min. :1.000 Min. : 0
## 1st Qu.:1.000 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.: 0
## Median :2.000 Median : 3.000 Median :1.000 Median : 85000
## Mean :2.394 Mean : 4.104 Mean :1.078 Mean : 54985
## 3rd Qu.:3.000 3rd Qu.: 5.000 3rd Qu.:1.000 3rd Qu.:100000
## Max. :4.000 Max. :22.000 Max. :2.000 Max. :220000
## satis
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.762
## 3rd Qu.:6.000
## Max. :7.000aggregate(mbasalary.df$salary, by=list(Sex=mbasalary.df$sex), mean)## Sex x
## 1 1 54373.45
## 2 2 56560.30aggregate(mbasalary.df$salary, by=list(FirstLanguage = mbasalary.df$frstlang), mean)## FirstLanguage x
## 1 1 54875.65
## 2 2 56286.67attach(mbasalary.df)
boxplot(salary~satis, horizontal=TRUE,col=c("violet","hotpink4","blue","green","yellow","orange","red"),xlab="salary",ylab="Level Of Satisfaction",main="Level of satisfaction Vs. salary")
boxplot(salary~sex, horizontal=TRUE,col=c("brown","yellow"), yaxt="n", main="Salary comparison based on sex", xlab="Salary",ylab="Sex",col.main="red")
axis(side = 2, at=c(1,2),labels = c("Male","Female"))
boxplot(salary~frstlang, horizontal=TRUE,col=c("brown","yellow"),yaxt="n",main="Salary comparison based on first language", xlab="Salary",ylab="First language",col.main="red")
axis(side = 2, at=c(1,2),labels = c("English","Non-English"))
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"Average salary of students: 54985
Average salary of females: 56560
Average salary of males: 54373
Average salary of students whose first language is English is higher than those who have others
Level Of satisfaction is healthy
library(car)
aggregate(cbind(gmat_tot,gmat_qpc,gmat_vpc,gmat_tpc)~sex, data=mbasalary.df, mean)## sex gmat_tot gmat_qpc gmat_vpc gmat_tpc
## 1 1 616.7626 80.92806 77.13669 83.58993
## 2 2 611.2963 75.27778 80.68519 83.20370aggregate(cbind(gmat_tot,gmat_qpc,gmat_vpc,gmat_tpc)~frstlang, data=mbasalary.df, mean)## frstlang gmat_tot gmat_qpc gmat_vpc gmat_tpc
## 1 1 616.7978 78.93258 79.50562 83.90449
## 2 2 596.6667 84.26667 61.80000 78.46667scatterplot(salary ~ gmat_tot, data=mbasalary.df, spread=FALSE, smoother.args=list(lty=2), pch=19,main="Salary Vs.GMAT Score", xlab="GMAT Score", ylab="Salary", col.lab="red",col.main="Orange")
It is clearly visible that GMAT Score plays a prominent role in getting good placement
Females have more average verbal GMAT_Score than males
Students without having their first_language as english have more average quantitative GMAT_Score
library(lattice)
attach(mbasalary.df)## The following objects are masked from mbasalary.df (pos = 5):
##
## age, f_avg, frstlang, gmat_qpc, gmat_tot, gmat_tpc, gmat_vpc,
## quarter, s_avg, salary, satis, sex, work_yrshistogram(salary~age, main="Distribution of Salary Vs. Age", xlab="Age", col.main="blue", col.lab="green")
library(car)
scatterplot(salary~gmat_qpc, data=mbasalary.df, main="Salary Vs. GMAT_Quantitative Percentile",xlab = "GMAT_Quantitative Score", ylab="Salary", col.main="red", col.lab="dark blue")
scatterplot(salary~gmat_vpc, data=mbasalary.df, main="Salary Vs. GMAT_Verbal Percentile",xlab = "GMAT_Verbal Percentile", ylab="Salary", col.main="red", col.lab="dark blue")
scatterplot(salary~gmat_tpc, data=mbasalary.df, main="Salary Vs. GMAT_Overall Percentile",xlab = "GMAT_Overall Percentile", ylab="Salary", col.main="red", col.lab="dark blue")
library(corrgram)
corrgram(mbasalary.df, upper.panel = panel.pie, lower.panel = panel.shade, text.panel = panel.txt, diag.panel = panel.minmax)
cor(mbasalary.df[,], use = "complete.obs", method="kendall")## age sex gmat_tot gmat_qpc gmat_vpc
## age 1.00000000 -0.065041153 -0.06082776 -0.1496542537 0.039907466
## sex -0.06504115 1.000000000 -0.03289126 -0.1162995642 0.068377056
## gmat_tot -0.06082776 -0.032891262 1.00000000 0.5912249323 0.618641641
## gmat_qpc -0.14965425 -0.116299564 0.59122493 1.0000000000 0.172322047
## gmat_vpc 0.03990747 0.068377056 0.61864164 0.1723220471 1.000000000
## gmat_tpc -0.06420357 -0.033917247 0.97893742 0.5884532087 0.610528597
## s_avg 0.08799754 0.056772894 0.07981602 0.0007924996 0.136687052
## f_avg 0.03212006 0.066136238 0.08645074 0.0593444969 0.075463243
## quarter -0.02647261 -0.079821423 -0.05206960 0.0205092140 -0.111510295
## work_yrs 0.66623303 0.006852418 -0.13837749 -0.2213521405 -0.007608988
## frstlang 0.11550298 -0.008488358 -0.07922043 0.0770445630 -0.174081341
## salary -0.04236378 -0.050931734 0.01423837 -0.0156051967 0.008744069
## satis -0.09804344 -0.046061133 0.05858051 -0.0419066838 0.112920113
## gmat_tpc s_avg f_avg quarter work_yrs
## age -0.06420357 0.0879975393 0.03212006 -0.02647261 0.666233029
## sex -0.03391725 0.0567728938 0.06613624 -0.07982142 0.006852418
## gmat_tot 0.97893742 0.0798160156 0.08645074 -0.05206960 -0.138377494
## gmat_qpc 0.58845321 0.0007924996 0.05934450 0.02050921 -0.221352141
## gmat_vpc 0.61052860 0.1366870519 0.07546324 -0.11151030 -0.007608988
## gmat_tpc 1.00000000 0.0749349137 0.08142346 -0.04555033 -0.136515221
## s_avg 0.07493491 1.0000000000 0.58736099 -0.69252193 0.106099286
## f_avg 0.08142346 0.5873609923 1.00000000 -0.44595369 0.028066988
## quarter -0.04555033 -0.6925219295 -0.44595369 1.00000000 -0.067650823
## work_yrs -0.13651522 0.1060992864 0.02806699 -0.06765082 1.000000000
## frstlang -0.07920923 -0.1098567638 -0.08235700 0.08331854 -0.008849845
## salary 0.01459666 0.0937391801 0.03816175 -0.15928756 0.028695844
## satis 0.06319036 -0.0278855407 -0.06652151 0.05724563 -0.040900067
## frstlang salary satis
## age 0.115502981 -0.042363777 -0.09804344
## sex -0.008488358 -0.050931734 -0.04606113
## gmat_tot -0.079220435 0.014238370 0.05858051
## gmat_qpc 0.077044563 -0.015605197 -0.04190668
## gmat_vpc -0.174081341 0.008744069 0.11292011
## gmat_tpc -0.079209234 0.014596662 0.06319036
## s_avg -0.109856764 0.093739180 -0.02788554
## f_avg -0.082356997 0.038161753 -0.06652151
## quarter 0.083318544 -0.159287562 0.05724563
## work_yrs -0.008849845 0.028695844 -0.04090007
## frstlang 1.000000000 0.001779082 -0.12505512
## salary 0.001779082 1.000000000 0.11802585
## satis -0.125055119 0.118025854 1.00000000Since probability of the sampled results is less than 0.01, we reject the null hypothesis
Model <- salary ~ work_yrs + s_avg + f_avg + gmat_qpc + gmat_vpc + sex + frstlang + satis+sex+satis+frstlang+quarter
fit1 <- lm(Model, data = placed.df)
summary(fit1)##
## Call:
## lm(formula = Model, data = placed.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30401 -8606 -979 4452 82897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 104978.1 33314.4 3.151 0.002188 **
## work_yrs 2327.1 587.3 3.962 0.000145 ***
## s_avg -157.4 8024.5 -0.020 0.984389
## f_avg -2098.4 3877.9 -0.541 0.589725
## gmat_qpc 106.9 122.6 0.872 0.385289
## gmat_vpc -100.6 103.4 -0.972 0.333360
## sex -4901.3 3589.1 -1.366 0.175363
## frstlang 13749.1 6663.7 2.063 0.041873 *
## satis -1327.7 2122.5 -0.626 0.533168
## quarter -2059.2 2674.2 -0.770 0.443244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15770 on 93 degrees of freedom
## Multiple R-squared: 0.2895, Adjusted R-squared: 0.2207
## F-statistic: 4.211 on 9 and 93 DF, p-value: 0.0001369``` ### Work years and First language are statistically correlated in our fitted model