getwd()
## [1] "C:/Users/TANAY/Downloads"
deans <- read.csv('Data - Deans Dilemma.csv')

head(deans)
##   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
placed <- subset(deans,Placement_B==1)

aggregate(Salary~Gender, data=placed, FUN = mean)
##   Gender   Salary
## 1      F 253068.0
## 2      M 284241.9
t.test(Salary~Gender, data=placed)
## 
##  Welch Two Sample t-test
## 
## data:  Salary by Gender
## t = -3.0757, df = 243.03, p-value = 0.00234
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -51138.42 -11209.22
## sample estimates:
## mean in group F mean in group M 
##        253068.0        284241.9
t.test(Salary~Gender, data=deans)
## 
##  Welch Two Sample t-test
## 
## data:  Salary by Gender
## t = -2.69, df = 278.55, p-value = 0.007577
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -66149.06 -10244.26
## sample estimates:
## mean in group F mean in group M 
##        193288.2        231484.8

Average salary of male MBAs who were placed= 284241.9 Average salary of female MBAs who were placed= 253068.0

p-value for placed = 0.00234 p-value for both, placed and non placed= 0.007577

INTERPRETATION OF t-test- In both cases, p<0.05 Thus, we can reject the null hypothesis. Hence, our hypothesis that “The average salary of the male MBAs is higher than the average salary of female MBAs.” is TRUE.