Deans dilemma
Siddhant Vats
January 25, 2018
Reading data through CSV file
mbaa.df <- read.csv(paste("Deans Dilemma.csv", sep=""))Getting summary stats:
summary(mbaa.df)## SlNo Gender Gender.B Percent_SSC Board_SSC
## Min. : 1.0 F:127 Min. :0.0000 Min. :37.00 CBSE :113
## 1st Qu.: 98.5 M:264 1st Qu.:0.0000 1st Qu.:56.00 ICSE : 77
## Median :196.0 Median :0.0000 Median :64.50 Others:201
## Mean :196.0 Mean :0.3248 Mean :64.65
## 3rd Qu.:293.5 3rd Qu.:1.0000 3rd Qu.:74.00
## Max. :391.0 Max. :1.0000 Max. :87.20
##
## Board_CBSE Board_ICSE Percent_HSC Board_HSC
## Min. :0.000 Min. :0.0000 Min. :40.0 CBSE : 96
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:54.0 ISC : 48
## Median :0.000 Median :0.0000 Median :63.0 Others:247
## Mean :0.289 Mean :0.1969 Mean :63.8
## 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:72.0
## Max. :1.000 Max. :1.0000 Max. :94.7
##
## Stream_HSC Percent_Degree Course_Degree
## Arts : 18 Min. :35.00 Arts : 13
## Commerce:222 1st Qu.:57.52 Commerce :117
## Science :151 Median :63.00 Computer Applications: 32
## Mean :62.98 Engineering : 37
## 3rd Qu.:69.00 Management :163
## Max. :89.00 Others : 5
## Science : 24
## Degree_Engg Experience_Yrs Entrance_Test S.TEST
## Min. :0.00000 Min. :0.0000 MAT :265 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 None : 67 1st Qu.:1.0000
## Median :0.00000 Median :0.0000 K-MAT : 24 Median :1.0000
## Mean :0.09463 Mean :0.4783 CAT : 22 Mean :0.8286
## 3rd Qu.:0.00000 3rd Qu.:1.0000 PGCET : 8 3rd Qu.:1.0000
## Max. :1.00000 Max. :3.0000 GCET : 2 Max. :1.0000
## (Other): 3
## Percentile_ET S.TEST.SCORE Percent_MBA
## Min. : 0.00 Min. : 0.00 Min. :50.83
## 1st Qu.:41.19 1st Qu.:41.19 1st Qu.:57.20
## Median :62.00 Median :62.00 Median :61.01
## Mean :54.93 Mean :54.93 Mean :61.67
## 3rd Qu.:78.00 3rd Qu.:78.00 3rd Qu.:66.02
## Max. :98.69 Max. :98.69 Max. :77.89
##
## Specialization_MBA Marks_Communication Marks_Projectwork
## Marketing & Finance:222 Min. :50.00 Min. :50.00
## Marketing & HR :156 1st Qu.:53.00 1st Qu.:64.00
## Marketing & IB : 13 Median :58.00 Median :69.00
## Mean :60.54 Mean :68.36
## 3rd Qu.:67.00 3rd Qu.:74.00
## Max. :88.00 Max. :87.00
##
## Marks_BOCA Placement Placement_B Salary
## Min. :50.00 Not Placed: 79 Min. :0.000 Min. : 0
## 1st Qu.:57.00 Placed :312 1st Qu.:1.000 1st Qu.:172800
## Median :63.00 Median :1.000 Median :240000
## Mean :64.38 Mean :0.798 Mean :219078
## 3rd Qu.:72.50 3rd Qu.:1.000 3rd Qu.:300000
## Max. :96.00 Max. :1.000 Max. :940000
## Plotting each important variable:
barplot(table(mbaa.df$Gender),main="Frequency distribution of gender",xlab = "Gender",ylab = "Frequency")
barplot(table(mbaa.df$Board_SSC),main="Frequency distribution of Different Boards",xlab = "Boards",ylab = "Frequency")
barplot(table(mbaa.df$Entrance_Test),main="Frequency distribution of Different Entrance test",xlab = "Test",ylab = "Frequency")
barplot(table(mbaa.df$Stream_HSC),main="Frequency distribution of Different stream",xlab = "Stream",ylab = "Frequency")
barplot(table(mbaa.df$Course_Degree),main="Frequency distribution of Different Course degree",xlab = "Course",ylab = "Frequency")
barplot(table(mbaa.df$Specialization_MBA),main="Frequency distribution of Different Specialization of MBA",xlab = "Specialization",ylab = "Frequency")
barplot(table(mbaa.df$Placement),main="Frequency distribution of status of placement",xlab = "Status of placement",ylab = "Frequency")
hist(mbaa.df$Marks_Communication,main="Distribution of marks of Communication",xlab="Marks",col = "grey")
hist(mbaa.df$Marks_Projectwork,main="Distribution of marks of Project",xlab="Marks",col = "grey")
hist(mbaa.df$Marks_BOCA,main="Distribution of marks of BOCA",xlab="Marks",col = "grey")
hist(mbaa.df$Salary,main="Distribution of Salary of placed students",xlab="Salary",col = "grey")
Plotting two related variable:
barplot(by(mbaa.df$Percent_SSC,mbaa.df$Board_SSC,sum),main = "Distribution of sum of marks of each board",xlab = "Board marks sum",ylab = "Total" )
barplot(by(mbaa.df$Percent_HSC,mbaa.df$Board_HSC,sum),main = "Distribution of sum of marks of each board",xlab = "Board marks sum",ylab = "Total" )
barplot(by(mbaa.df$Percent_HSC,mbaa.df$Course_Degree,sum),main = "Distribution of selection of course degree on the basis of there marks ",xlab = "Course degree",ylab = "Total" )
barplot(by(mbaa.df$Percent_Degree,mbaa.df$Course_Degree,sum),main = "Distribution of sum of marks of each Degree",xlab = "Degree marks sum",ylab = "Total" )
barplot(table(mbaa.df$Stream_HSC,mbaa.df$Board_HSC),main = "Distribution of boards and corresponding stream",xlab = "Board ",ylab = "Frequency" )
Plotting Corrgram
library(corrgram)
corrgram(mbaa.df, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of MBA variables")
Applying OLS regression:
fit<-lm(mbaa.df$Salary~ mbaa.df$Marks_Communication+mbaa.df$Marks_Projectwork+mbaa.df$Marks_BOCA+mbaa.df$Percent_MBA+mbaa.df$Specialization_MBA+mbaa.df$Gender)
summary(fit)##
## Call:
## lm(formula = mbaa.df$Salary ~ mbaa.df$Marks_Communication + mbaa.df$Marks_Projectwork +
## mbaa.df$Marks_BOCA + mbaa.df$Percent_MBA + mbaa.df$Specialization_MBA +
## mbaa.df$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -313284 -54850 23349 70037 672757
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -255474.9 92562.2 -2.760
## mbaa.df$Marks_Communication 671.3 1108.9 0.605
## mbaa.df$Marks_Projectwork 1905.0 1072.4 1.776
## mbaa.df$Marks_BOCA 1241.9 825.6 1.504
## mbaa.df$Percent_MBA 3013.2 1931.6 1.560
## mbaa.df$Specialization_MBAMarketing & HR -12163.0 14241.8 -0.854
## mbaa.df$Specialization_MBAMarketing & IB -23476.3 38498.8 -0.610
## mbaa.df$GenderM 64496.3 15437.0 4.178
## Pr(>|t|)
## (Intercept) 0.00606 **
## mbaa.df$Marks_Communication 0.54530
## mbaa.df$Marks_Projectwork 0.07646 .
## mbaa.df$Marks_BOCA 0.13334
## mbaa.df$Percent_MBA 0.11959
## mbaa.df$Specialization_MBAMarketing & HR 0.39362
## mbaa.df$Specialization_MBAMarketing & IB 0.54236
## mbaa.df$GenderM 3.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133300 on 383 degrees of freedom
## Multiple R-squared: 0.08781, Adjusted R-squared: 0.07114
## F-statistic: 5.267 on 7 and 383 DF, p-value: 9.296e-06confint(fit)## 2.5 % 97.5 %
## (Intercept) -437468.6303 -73481.239
## mbaa.df$Marks_Communication -1509.0354 2851.632
## mbaa.df$Marks_Projectwork -203.4990 4013.447
## mbaa.df$Marks_BOCA -381.3755 2865.235
## mbaa.df$Percent_MBA -784.6002 6810.941
## mbaa.df$Specialization_MBAMarketing & HR -40164.8794 15838.959
## mbaa.df$Specialization_MBAMarketing & IB -99171.7321 52219.189
## mbaa.df$GenderM 34144.3318 94848.216coefficients(fit)## (Intercept)
## -255474.9345
## mbaa.df$Marks_Communication
## 671.2983
## mbaa.df$Marks_Projectwork
## 1904.9742
## mbaa.df$Marks_BOCA
## 1241.9296
## mbaa.df$Percent_MBA
## 3013.1706
## mbaa.df$Specialization_MBAMarketing & HR
## -12162.9602
## mbaa.df$Specialization_MBAMarketing & IB
## -23476.2714
## mbaa.df$GenderM
## 64496.2738Summary: Moving left to right values decreases
- Male(250)>>Female(150)
- CBSE>>ICSE>>OTHERS 3)Mat(265)>>None(67)>>K-Mat(24)>>PGCET(8)>>CAT(22)>>GCET(2)>>(G-MAT,G-SAT,XAT)
- Commerece(>200)>>Science(150)>>Arts(50) 5)Management(>150)>>Commerce(<150)>>Engineer(<50)>>CScience>>Sci>>Art>>Oth
- Mar & Finance(>200)>>Mar & HR(150)>>Mar & IB(<50)
- Placed(300)>>NOT Placed(<100)
- Marks in comm are mostlly in range 50 to 55 9)Project marks are mostlly on range 65 to 75 10)Boca marks are mostlly in range of 50 to 65 11)Salary are mostlly in range of 2 to 3 Lakh. 12)Boards Marks Others>>CBSE>>ISCE For 10th. 13)Boards Marks Others>>CBSE>>ISC For 12th. 14)Management(>10000)>>Commerce(>8000)>>Engineer(<4000)>>CScience(<2000)>>Sci>>Art>>Oth 15)Marks in each stream is Commerce(1,47,676)>>Science(9,024)>>Arts(1,152)