Read the dataset

data1<-read.csv(paste("Data - Deans Dilemma.csv", sep=""))
head(data1)
##   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

Create a dataframe called placed containing a subset of only those students who were successfully placed

placed.df<-data1[which(data1$Placement_B=='1'),]
head(placed.df)
##   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

Mean salary of males and females who were placed.

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

Run a t-test to test the following hypothesis: H1: The average salary of the male MBAs is higher than the average salary of female MBAs.

t.test(placed.df$Salary~placed.df$Gender,var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  placed.df$Salary by placed.df$Gender
## t = -2.7597, df = 310, p-value = 0.00613
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -53400.627  -8947.012
## sample estimates:
## mean in group F mean in group M 
##        253068.0        284241.9

Interpret the results

  1. R code that creates a table showing the mean salary of males and females, who were placed is written
  2. Mean Salary for Males who were placed = 284241.9
  3. Mean Salary for Females who were placed = 253068.0
  4. R code for t-test for the Hypothesis “The average salary of the male MBAs is higher than the average salary of female MBAs.” is written
  5. The p-value based on the t-test = 0.00613
  6. Meaning of the t-test, as applied to the average salaries of male and female MBAs - The t test showed there was a significant difference in average salaries between males and females. We can now reject the null hypothesis that there is no significant difference between the salaries of males and females. The average salaries of males is greater than that of females.