Dean’s Dilemma
Tanya Gupta
12/9/2017
The analysis is based on the dataset Data - Deans Dilemma.csv.
This file explains the meaning of each column in the given dataset.
Task 2b
Using the read.csv() function in R, data is read and stored in a dataframe, in this case - dilemma.df
dilemma.df<-read.csv("Data - Deans Dilemma.csv")Using the View() function in R, the dataframe can be viewed.
View(dilemma.df)Task 2c
The summary statistics (e.g. mean, standard deviation, median, mode) can be created for the important variables in the dataset as follows:
summary(dilemma.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
## Alternately, the psych library can also be used as follows:
library(psych)
describe(dilemma.df)## vars n mean sd median trimmed
## SlNo 1 391 196.00 113.02 196.00 196.00
## Gender* 2 391 1.68 0.47 2.00 1.72
## Gender.B 3 391 0.32 0.47 0.00 0.28
## Percent_SSC 4 391 64.65 10.96 64.50 64.76
## Board_SSC* 5 391 2.23 0.87 3.00 2.28
## Board_CBSE 6 391 0.29 0.45 0.00 0.24
## Board_ICSE 7 391 0.20 0.40 0.00 0.12
## Percent_HSC 8 391 63.80 11.42 63.00 63.34
## Board_HSC* 9 391 2.39 0.85 3.00 2.48
## Stream_HSC* 10 391 2.34 0.56 2.00 2.36
## Percent_Degree 11 391 62.98 8.92 63.00 62.91
## Course_Degree* 12 391 3.85 1.61 4.00 3.81
## Degree_Engg 13 391 0.09 0.29 0.00 0.00
## Experience_Yrs 14 391 0.48 0.67 0.00 0.36
## Entrance_Test* 15 391 5.85 1.35 6.00 6.08
## S.TEST 16 391 0.83 0.38 1.00 0.91
## Percentile_ET 17 391 54.93 31.17 62.00 56.87
## S.TEST.SCORE 18 391 54.93 31.17 62.00 56.87
## Percent_MBA 19 391 61.67 5.85 61.01 61.45
## Specialization_MBA* 20 391 1.47 0.56 1.00 1.42
## Marks_Communication 21 391 60.54 8.82 58.00 59.68
## Marks_Projectwork 22 391 68.36 7.15 69.00 68.60
## Marks_BOCA 23 391 64.38 9.58 63.00 64.08
## Placement* 24 391 1.80 0.40 2.00 1.87
## Placement_B 25 391 0.80 0.40 1.00 0.87
## Salary 26 391 219078.26 138311.65 240000.00 217011.50
## mad min max range skew kurtosis
## SlNo 145.29 1.00 391.00 390.00 0.00 -1.21
## Gender* 0.00 1.00 2.00 1.00 -0.75 -1.45
## Gender.B 0.00 0.00 1.00 1.00 0.75 -1.45
## Percent_SSC 12.60 37.00 87.20 50.20 -0.06 -0.72
## Board_SSC* 0.00 1.00 3.00 2.00 -0.45 -1.53
## Board_CBSE 0.00 0.00 1.00 1.00 0.93 -1.14
## Board_ICSE 0.00 0.00 1.00 1.00 1.52 0.31
## Percent_HSC 13.34 40.00 94.70 54.70 0.29 -0.67
## Board_HSC* 0.00 1.00 3.00 2.00 -0.83 -1.13
## Stream_HSC* 0.00 1.00 3.00 2.00 -0.12 -0.72
## Percent_Degree 8.90 35.00 89.00 54.00 0.05 0.24
## Course_Degree* 1.48 1.00 7.00 6.00 0.00 -1.08
## Degree_Engg 0.00 0.00 1.00 1.00 2.76 5.63
## Experience_Yrs 0.00 0.00 3.00 3.00 1.27 1.17
## Entrance_Test* 0.00 1.00 9.00 8.00 -2.52 7.04
## S.TEST 0.00 0.00 1.00 1.00 -1.74 1.02
## Percentile_ET 25.20 0.00 98.69 98.69 -0.74 -0.69
## S.TEST.SCORE 25.20 0.00 98.69 98.69 -0.74 -0.69
## Percent_MBA 6.39 50.83 77.89 27.06 0.34 -0.52
## Specialization_MBA* 0.00 1.00 3.00 2.00 0.70 -0.56
## Marks_Communication 8.90 50.00 88.00 38.00 0.74 -0.25
## Marks_Projectwork 7.41 50.00 87.00 37.00 -0.26 -0.27
## Marks_BOCA 11.86 50.00 96.00 46.00 0.29 -0.85
## Placement* 0.00 1.00 2.00 1.00 -1.48 0.19
## Placement_B 0.00 0.00 1.00 1.00 -1.48 0.19
## Salary 88956.00 0.00 940000.00 940000.00 0.24 1.74
## se
## SlNo 5.72
## Gender* 0.02
## Gender.B 0.02
## Percent_SSC 0.55
## Board_SSC* 0.04
## Board_CBSE 0.02
## Board_ICSE 0.02
## Percent_HSC 0.58
## Board_HSC* 0.04
## Stream_HSC* 0.03
## Percent_Degree 0.45
## Course_Degree* 0.08
## Degree_Engg 0.01
## Experience_Yrs 0.03
## Entrance_Test* 0.07
## S.TEST 0.02
## Percentile_ET 1.58
## S.TEST.SCORE 1.58
## Percent_MBA 0.30
## Specialization_MBA* 0.03
## Marks_Communication 0.45
## Marks_Projectwork 0.36
## Marks_BOCA 0.48
## Placement* 0.02
## Placement_B 0.02
## Salary 6994.72Task 3a
The median salary of all the students in the data sample can be obtained from the following:
summary(dilemma.df$Salary)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 172800 240000 219100 300000 940000Therefore, median salary of all students in data sample = 240000
Task 3b
The percentage(rounded to 2 decimal places) of students who were placed can be observed from the following:
mytable <- xtabs(~ Placement_B, data=dilemma.df)
format(round(prop.table(mytable)*100, 2), nsmall = 2)## Placement_B
## 0 1
## "20.20" "79.80"Therefore, percentage of students who were placed = 79.80%
Task 3c
A dataframe called placed.df is created as follows, that contains a subset of only those students who were successfully placed.
placed.df<-dilemma.df[which(dilemma.df$Placement_B==1),]Task 3d
The median salary of the placed students in the data sample can be obtained from the following:
summary(placed.df$Salary)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 120000 220000 260000 274600 300000 940000Therefore, median salary of placed students = 260000
Task 3e
A table showing the mean salary of males and females, who were placed can be created as follows:
by(placed.df$Salary, placed.df$Gender, mean)## placed.df$Gender: F
## [1] 253068
## --------------------------------------------------------
## placed.df$Gender: M
## [1] 284241.9Alternately,
aggregate(placed.df$Salary, by=list(placed.df$Gender), mean) #alternate## Group.1 x
## 1 F 253068.0
## 2 M 284241.9Therefore,
average salary of placed males = 284241.9
average salary of placed females = 284241.9
Task 3f
A histogram showing a breakup of the MBA performance of the students who were placed can be generated as follows:
performance = placed.df$Percent_MBA
hist(performance, right=FALSE, xlim = c(50, 80), breaks = 3, main="MBA Performance of placed students", xlab="MBA percentage", ylab="count")
Task 3g
A dataframe called notplaced.df is created as follows, that contains a subset of only those students who were NOT placed after their MBA.
notplaced.df<-dilemma.df[which(dilemma.df$Placement_B==0),]Task 3h
Two histograms can be generated side-by-side, visually comparing the MBA performance of Placed and Not Placed students, as follows:
layout(matrix(c(1, 1, 2, 2), 1, 4, byrow = TRUE), widths = c(1,1))
hist(placed.df$Percent_MBA, right=FALSE, xlim = c(50, 80), breaks = 3, main="MBA Performance of placed students", xlab="MBA percentage", ylab="count")
hist(notplaced.df$Percent_MBA, right=FALSE, xlim = c(50, 80), breaks = 3, main="MBA Performance of not placed students", xlab="MBA percentage", ylab="count")
Task 3i
Two boxplots can be generated, one below the other, comparing the distribution of salaries of males and females who were placed, as follows:
boxplot(placed.df$Salary ~ placed.df$Gender, horizontal=TRUE, ylab = "Gender", xlab = "Salary", las=1, main="Comparison of Salaries of Males and Females")
Task 3j
A dataframe called placedET.df is created as follows, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program.
placedET.df<-placed.df[which(placed.df$S.TEST==1),]Task 3k
A Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET.df can be drawn as follows:
library(car)
scatterplotMatrix(formula = ~ Salary+Percent_MBA+Percentile_ET, cex=0.6, data=placedET.df, diagonal="density")