1) Dean’s Dilemma Dataset

      dean.df <- read.csv(paste("Data - Deans Dilemma.csv", sep=""))
      View(dean.df)

2) Summarizing the dataset

    summary(dean.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  
##

    library(psych)
    describe(dean.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              6994.72

3) Meadian Salary of all students in the data sample

    median(dean.df$Salary)

## [1] 240000

We see that the median salary is 240000

4) Percentage of students who were placed

    x <- prop.table(table(dean.df$Placement_B))*100
    format(round(x, 2), nsmall = 2)

## 
##       0       1 
## "20.20" "79.80"

Percentage of students who were placed upto 2 decimal places is 79.80%

5) New dataset containing only placed students

    placed <- dean.df[which(dean.df$Placement_B=='1'),]
    View(placed)

  The new dataset "placed" containing details of all students who are placed has been created

6) Median salary of students who were placed

    median(placed$Salary)

## [1] 260000

The median salary of all the placed students is 260000

7) Table Showing mean Salaries of males and females who were placed

    aggregate(placed$Salary, by=list(gender=placed$Gender), mean)

##   gender        x
## 1      F 253068.0
## 2      M 284241.9

  The mean salary of females who were placed is 253068.0, and the mean salary of males who         were placed is 284241.9.

8) Histogram showing breakup of MBA performance of students who were placed

    hist(placed$Percent_MBA,
         main = "MBA Performance of placed students",
         xlab = "MBA Percentage",
         ylab = "Count",
         breaks=2,
         col= " grey")

The required histogram has been plotted

9) Data frame containing unplaced students only

    unplaced <- dean.df[which(dean.df$Placement_B=='0'),]
    View(unplaced)

10) Histograms comparing Placed and Unplaced Students’ Performance

   par(mfrow=c(1, 2))
    hist(placed$Percent_MBA,
         main = "MBA Performance of placed students",
         xlab = "MBA Percentage",
         ylab = "Count",
         breaks=2,
         col= " grey")
    hist(unplaced$Percent_MBA,
         main = "MBA Performance of not placed students",
         xlab = "MBA Percentage",
         ylab = "Count",
         breaks=2,
         col= " grey")

  The two histograms have been plotted

11) Boxplots comparing the distribution of salaries of males and females who were placed

    boxplot(Salary~Gender, data=placed,horizontal=TRUE, 
            ylab= " Gender", xlab=" Salary",
            main=" Comparison of Salaries of Males and Females")
    axis(side=2, at=c(1,2), labels=c("Females","Males"))

12) Creating a new dataframe placedET

  placedET<- dean.df[which(dean.df$Placement_B=='1' & dean.df$S.TEST=='1'),]
  View(placedET)

13) Scatter Plot Matrix

    library(car)
    scatterplotMatrix(formula = ~Salary + Percent_MBA + Percentile_ET, cex=0.6,
                      data=placedET, diagonal ="density")

Dean’s Dilemma

Aditya Satheesh

9 December 2017

1) Dean’s Dilemma Dataset

2) Summarizing the dataset

3) Meadian Salary of all students in the data sample

4) Percentage of students who were placed

5) New dataset containing only placed students

6) Median salary of students who were placed

7) Table Showing mean Salaries of males and females who were placed

8) Histogram showing breakup of MBA performance of students who were placed

9) Data frame containing unplaced students only

10) Histograms comparing Placed and Unplaced Students’ Performance

11) Boxplots comparing the distribution of salaries of males and females who were placed

12) Creating a new dataframe placedET

13) Scatter Plot Matrix