Importing the data:

dean_dil <- read.csv(paste("Data - Deans Dilemma.csv",sep = ""))
head(dean_dil)
##   SlNo Gender Gender.B Percent_SSC Board_SSC Board_CBSE Board_ICSE
## 1    1      M        0       62.00    Others          0          0
## 2    2      M        0       76.33      ICSE          0          1
## 3    3      M        0       72.00    Others          0          0
## 4    4      M        0       60.00      CBSE          1          0
## 5    5      M        0       61.00      CBSE          1          0
## 6    6      M        0       55.00      ICSE          0          1
##   Percent_HSC Board_HSC Stream_HSC Percent_Degree         Course_Degree
## 1       88.00    Others   Commerce          52.00               Science
## 2       75.33    Others    Science          75.48 Computer Applications
## 3       78.00    Others   Commerce          66.63           Engineering
## 4       63.00      CBSE       Arts          58.00            Management
## 5       55.00       ISC    Science          54.00           Engineering
## 6       64.00      CBSE   Commerce          50.00              Commerce
##   Degree_Engg Experience_Yrs Entrance_Test S.TEST Percentile_ET
## 1           0              0           MAT      1          55.0
## 2           0              1           MAT      1          86.5
## 3           1              0          None      0           0.0
## 4           0              0           MAT      1          75.0
## 5           1              1           MAT      1          66.0
## 6           0              0          None      0           0.0
##   S.TEST.SCORE Percent_MBA  Specialization_MBA Marks_Communication
## 1         55.0       58.80      Marketing & HR                  50
## 2         86.5       66.28 Marketing & Finance                  69
## 3          0.0       52.91 Marketing & Finance                  50
## 4         75.0       57.80 Marketing & Finance                  54
## 5         66.0       59.43      Marketing & HR                  52
## 6          0.0       56.81 Marketing & Finance                  53
##   Marks_Projectwork Marks_BOCA Placement Placement_B Salary
## 1                65         74    Placed           1 270000
## 2                70         75    Placed           1 200000
## 3                61         59    Placed           1 240000
## 4                66         62    Placed           1 250000
## 5                65         67    Placed           1 180000
## 6                70         53    Placed           1 300000

Subset of placed students:

placed <- dean_dil[which(dean_dil$Placement_B == '1'),]
head(placed)
##   SlNo Gender Gender.B Percent_SSC Board_SSC Board_CBSE Board_ICSE
## 1    1      M        0       62.00    Others          0          0
## 2    2      M        0       76.33      ICSE          0          1
## 3    3      M        0       72.00    Others          0          0
## 4    4      M        0       60.00      CBSE          1          0
## 5    5      M        0       61.00      CBSE          1          0
## 6    6      M        0       55.00      ICSE          0          1
##   Percent_HSC Board_HSC Stream_HSC Percent_Degree         Course_Degree
## 1       88.00    Others   Commerce          52.00               Science
## 2       75.33    Others    Science          75.48 Computer Applications
## 3       78.00    Others   Commerce          66.63           Engineering
## 4       63.00      CBSE       Arts          58.00            Management
## 5       55.00       ISC    Science          54.00           Engineering
## 6       64.00      CBSE   Commerce          50.00              Commerce
##   Degree_Engg Experience_Yrs Entrance_Test S.TEST Percentile_ET
## 1           0              0           MAT      1          55.0
## 2           0              1           MAT      1          86.5
## 3           1              0          None      0           0.0
## 4           0              0           MAT      1          75.0
## 5           1              1           MAT      1          66.0
## 6           0              0          None      0           0.0
##   S.TEST.SCORE Percent_MBA  Specialization_MBA Marks_Communication
## 1         55.0       58.80      Marketing & HR                  50
## 2         86.5       66.28 Marketing & Finance                  69
## 3          0.0       52.91 Marketing & Finance                  50
## 4         75.0       57.80 Marketing & Finance                  54
## 5         66.0       59.43      Marketing & HR                  52
## 6          0.0       56.81 Marketing & Finance                  53
##   Marks_Projectwork Marks_BOCA Placement Placement_B Salary
## 1                65         74    Placed           1 270000
## 2                70         75    Placed           1 200000
## 3                61         59    Placed           1 240000
## 4                66         62    Placed           1 250000
## 5                65         67    Placed           1 180000
## 6                70         53    Placed           1 300000

3b. Table showing the average salary of males and females, who were placed

aggregate(Salary ~ Gender, data = placed, mean)
##   Gender   Salary
## 1      F 253068.0
## 2      M 284241.9

3c. t-test to test hypothesis H1: The average salary of the male MBAs is higher than the average salary of female MBAs

Our NULL Hypothesis would be “H2: There is no significant difference between average salaries of male and female MBA graduates.”

attach(placed)
log.transformed.Salary=log(Salary)
t.test(log.transformed.Salary~Gender,var.equal=TRUE)
## 
##  Two Sample t-test
## 
## data:  log.transformed.Salary by Gender
## t = -2.8142, df = 310, p-value = 0.005203
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.17482594 -0.03094897
## sample estimates:
## mean in group F mean in group M 
##        12.40435        12.50723

Interpretation :

  1. Average salary of Male graduates is 284241.9 and female graduates is 253068.0

2.Since, p-value is significantly less than 0.05 we reject our NULL hypothesis H2.