Dean Case Study
Jiji
February 3, 2018
R Markdown
mba <- read.csv(paste("Deans Dilemma.csv", sep=""))
View(mba)Attach the dataframe
attach(mba)Summarize the data
summary(mba)## 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(mba)## 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 6994.72Q1. What is the median salary of all the students?
median(mba$Salary)## [1] 240000Q2. What percentage of students were placed? Answer rounding off to 2 decimal places.
summary(mba$Placement)## Not Placed Placed
## 79 312100*mean(mba$Placement_B)## [1] 79.7954Q3. Create a dataframe called “placed” having the subset of students who were placed
placed <- mba[ which(mba$Placement=='Placed') , ] Q4. What is the median salary of students who were placed, excluding those who were not placed?
Method 1:
median (mba$Salary[mba$Salary > 0] )## [1] 260000Method 2:
median(placed$Salary)## [1] 260000Q5. Considering placed students, create a table giving the mean salary of males and females?
Method 1: Using aggregate()
aggregate(placed$Salary, by=list(Sex = placed$Gender), mean)## Sex x
## 1 F 253068.0
## 2 M 284241.9Method 2: Using by()
by(placed$Salary, placed$Gender, mean)## placed$Gender: F
## [1] 253068
## --------------------------------------------------------
## placed$Gender: M
## [1] 284241.9Q7. Create a dataframe called notplaced, containing only those students
who were not placed.
notplaced <- mba[ which(mba$Placement=='Not Placed') , ] Q10. Create a dataframe called placedET, representing students who were placed
after the MBA and who also gave some MBA entrance test
before admission into the MBA program.
Method 1:
placedET <- placed[ which(placed$S.TEST==1) , ] Method 2:
placedET <- placed[ which(placed$Entrance_Test != "None") , ]
View(placedET)Including Plots
You can also embed plots, for example: ###Q11.generate the following histogram showing a breakup of the MBA performance of the students who were placed
hist(placed$Percent_MBA , main = "MBA Performance of placed students" , xlab = "MBA Percentage" , ylab = "Count" , xlim = c(50,80) , ylim = c(0,150) , col = "grey" , breaks = c(50,seq(60,80,10)))
###Q12.Dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA.
notplaced <- subset(mba , Placement == "Not Placed" , select = c(Percent_MBA , Gender , Salary))
notplaced## Percent_MBA Gender Salary
## 11 69.78 F 0
## 16 53.29 F 0
## 20 54.65 M 0
## 40 67.28 F 0
## 42 51.75 F 0
## 43 56.34 M 0
## 59 51.29 M 0
## 64 52.56 M 0
## 68 51.45 M 0
## 75 51.21 M 0
## 79 71.63 F 0
## 82 56.11 F 0
## 88 56.19 M 0
## 89 65.49 F 0
## 94 61.31 M 0
## 98 60.29 M 0
## 100 56.45 F 0
## 109 72.00 M 0
## 112 54.76 F 0
## 128 71.15 F 0
## 144 67.13 F 0
## 149 55.83 M 0
## 162 58.00 M 0
## 166 55.41 M 0
## 175 59.47 M 0
## 176 64.95 F 0
## 184 55.30 M 0
## 191 56.09 M 0
## 194 60.64 M 0
## 204 58.81 M 0
## 220 64.15 M 0
## 226 62.29 F 0
## 231 62.83 F 0
## 236 57.32 F 0
## 240 61.90 M 0
## 241 61.22 M 0
## 248 58.52 M 0
## 252 52.32 M 0
## 258 55.87 M 0
## 266 65.99 M 0
## 269 61.87 M 0
## 272 65.13 M 0
## 284 74.56 F 0
## 285 54.99 M 0
## 287 75.71 M 0
## 288 57.16 M 0
## 289 58.79 F 0
## 290 65.48 M 0
## 291 69.28 F 0
## 298 67.44 F 0
## 301 60.69 M 0
## 307 72.14 F 0
## 310 60.02 M 0
## 315 63.83 F 0
## 317 59.81 M 0
## 318 61.66 F 0
## 322 57.29 F 0
## 324 62.93 F 0
## 326 56.13 M 0
## 328 66.94 F 0
## 329 63.94 M 0
## 330 62.50 F 0
## 332 66.18 M 0
## 336 64.74 M 0
## 338 65.28 M 0
## 344 63.53 F 0
## 350 58.44 M 0
## 352 72.21 F 0
## 355 51.48 M 0
## 364 53.39 M 0
## 375 62.42 F 0
## 376 60.22 M 0
## 377 52.36 M 0
## 379 56.00 M 0
## 382 50.83 M 0
## 383 56.81 F 0
## 384 59.14 M 0
## 386 67.94 M 0
## 391 60.36 M 0Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot. ###Q13. Draw two histograms side-by-side, visually comparing the MBA performance of Placed and Not Placed students.
par(mfrow = c(1,2))
hist(placedET$Percent_MBA , main = "MBA Performance of placed students" , xlab = "MBA Percentage" , ylab = "Count" , xlim = c(50,80) , ylim = c(0,150) , col = "grey" , breaks = c(50,seq(60,80,10)))
hist(notplaced$Percent_MBA , main = "MBA Performance of not placed students" , xlab = "MBA Percentage" , ylab = "Count" , xlim = c(50,80) , ylim = c(0,40) , col = "grey" , breaks = c(50,seq(60,80,10)))
###Q14.Draw two boxplots, one below the other, comparing the distribution of salaries of males and females who were placed.
par(mfrow = c(1,1))
library(lattice)
bwplot(Gender ~ Salary , data = placedET, horizontal = TRUE , xlab = "Salary" , ylab = "Gender" , main = "Comparison of Salaries of Males and Females")
###Q15 Create a dataframe called placedET, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program
placedET <- subset(mba , Placement == "Placed" & S.TEST == 1 , select = c(Salary , Percent_MBA , Percentile_ET))
placedET## Salary Percent_MBA Percentile_ET
## 1 270000 58.80 55.00
## 2 200000 66.28 86.50
## 4 250000 57.80 75.00
## 5 180000 59.43 66.00
## 8 235000 57.23 43.12
## 9 425000 55.50 96.80
## 12 250000 54.01 79.00
## 13 180000 51.58 55.00
## 15 450000 58.21 33.00
## 19 252000 62.14 67.00
## 21 280000 63.26 70.00
## 22 231000 61.29 91.34
## 23 224000 62.51 35.00
## 24 120000 52.21 54.00
## 25 260000 60.85 62.00
## 26 300000 60.77 75.00
## 28 120000 58.56 49.00
## 29 250000 63.70 60.00
## 30 180000 65.04 62.00
## 31 218000 68.63 68.00
## 33 150000 54.96 76.00
## 34 250000 64.19 48.00
## 35 200000 64.66 72.00
## 36 300000 62.54 60.00
## 37 330000 52.41 79.00
## 38 265000 56.61 0.00
## 39 340000 61.83 70.00
## 41 177600 64.08 68.00
## 44 236000 77.89 50.48
## 45 265000 56.70 50.00
## 47 393000 69.06 95.00
## 48 360000 68.81 55.53
## 49 300000 63.62 92.00
## 51 360000 74.01 97.40
## 52 180000 65.33 76.00
## 53 180000 62.80 74.00
## 55 240000 57.55 94.00
## 56 300000 60.76 41.38
## 57 265000 57.69 68.00
## 58 350000 64.15 73.35
## 60 250000 56.70 52.00
## 61 180000 58.32 64.00
## 62 278000 62.21 50.89
## 63 150000 57.61 83.00
## 65 260000 72.78 88.00
## 66 180000 62.77 68.44
## 67 300000 62.74 71.00
## 69 400000 68.85 0.00
## 70 320000 55.47 58.00
## 71 240000 56.86 53.70
## 72 411000 62.56 93.00
## 73 287000 66.72 60.00
## 74 198000 69.76 65.00
## 76 300000 62.90 95.00
## 77 200000 69.70 89.00
## 78 180000 66.53 58.00
## 80 204000 54.55 78.00
## 81 250000 62.46 64.00
## 83 200000 62.98 65.00
## 84 275000 62.27 97.33
## 85 192000 62.65 67.00
## 87 300000 60.91 53.00
## 90 450000 71.04 87.00
## 91 216000 65.56 78.00
## 92 220000 52.71 71.00
## 95 300000 67.31 68.00
## 96 240000 66.88 68.00
## 97 360000 63.59 80.00
## 99 268000 57.99 74.00
## 101 265000 56.66 57.60
## 102 260000 57.24 60.00
## 104 300000 62.48 61.60
## 105 240000 59.69 59.00
## 107 240000 64.75 44.56
## 108 400000 57.76 13.00
## 111 250000 76.72 78.00
## 114 180000 59.50 68.50
## 116 240000 58.78 61.00
## 117 120000 57.10 89.69
## 119 275000 58.46 68.92
## 120 275000 60.99 68.71
## 121 150000 59.24 79.00
## 122 275000 68.07 70.00
## 124 240000 58.75 41.00
## 126 360000 65.45 89.00
## 127 280000 62.40 46.92
## 129 325000 60.43 50.00
## 130 204000 60.76 40.00
## 131 240000 66.94 95.00
## 132 240000 68.53 95.50
## 133 336000 61.41 96.00
## 134 218000 59.75 86.00
## 136 336000 67.20 84.27
## 137 190000 67.00 74.00
## 138 230000 64.27 61.00
## 139 390000 51.24 94.30
## 140 500000 57.65 69.00
## 141 270000 59.42 86.04
## 142 150000 67.99 75.00
## 143 240000 62.35 67.00
## 145 276000 62.01 40.00
## 146 300000 70.20 86.00
## 147 168000 60.44 82.00
## 148 300000 66.69 84.00
## 150 270000 59.81 0.00
## 152 300000 62.00 55.00
## 153 400000 76.18 78.74
## 154 220000 57.03 67.00
## 155 180000 59.08 75.00
## 156 180000 58.85 64.00
## 157 210000 64.36 58.00
## 158 210000 62.36 62.00
## 159 300000 68.03 92.00
## 160 290000 66.86 92.00
## 161 180000 62.79 67.00
## 163 230000 59.47 72.00
## 164 282000 64.63 47.41
## 165 260000 53.57 29.00
## 167 180000 66.50 56.39
## 168 260000 54.97 53.88
## 169 400000 56.51 79.00
## 170 420000 62.16 95.46
## 172 300000 64.44 66.00
## 173 150000 69.03 93.91
## 174 220000 57.31 70.00
## 177 380000 60.44 78.00
## 179 300000 61.31 57.50
## 180 252000 55.42 67.00
## 181 280000 63.39 58.00
## 182 240000 65.83 85.00
## 183 360000 58.23 55.00
## 185 180000 65.69 71.00
## 186 450000 67.83 95.00
## 187 200000 73.52 80.00
## 188 300000 58.31 84.00
## 193 250000 54.80 57.20
## 195 250000 53.94 58.00
## 196 280000 63.08 72.15
## 197 250000 55.01 53.70
## 198 216000 60.50 89.00
## 199 204000 52.42 39.00
## 200 300000 70.85 96.00
## 201 240000 67.05 80.00
## 202 276000 70.48 97.00
## 203 940000 64.34 82.66
## 205 250000 71.49 55.67
## 206 300000 59.99 85.00
## 207 180000 57.98 14.99
## 208 236000 71.00 80.40
## 209 240000 56.70 60.00
## 210 250000 61.26 64.00
## 211 350000 73.33 75.00
## 213 210000 68.20 70.00
## 214 250000 58.40 55.50
## 215 400000 76.26 81.20
## 216 300000 70.71 84.00
## 217 480000 61.79 86.00
## 218 250000 68.55 90.00
## 219 320000 67.54 89.95
## 221 385000 69.94 65.00
## 222 360000 60.78 80.00
## 223 300000 53.49 74.40
## 225 250000 60.98 65.00
## 227 250000 67.13 94.00
## 228 275000 58.73 43.00
## 229 200000 65.63 55.60
## 230 150000 61.58 78.00
## 232 300000 60.95 65.00
## 233 225000 60.41 56.00
## 235 250000 71.77 96.00
## 237 220000 54.43 58.00
## 239 265000 56.94 56.00
## 242 260000 61.29 60.00
## 243 300000 60.39 89.00
## 244 180000 51.73 39.00
## 245 530000 59.54 65.00
## 246 156000 56.75 66.60
## 247 263000 58.95 40.00
## 249 400000 63.23 72.00
## 250 233000 55.14 85.00
## 251 300000 62.28 83.00
## 253 240000 64.08 57.00
## 254 180000 58.54 64.25
## 256 198000 55.67 40.00
## 259 690000 61.30 56.00
## 260 270000 58.87 83.00
## 261 240000 65.25 98.00
## 263 340000 62.48 86.00
## 264 250000 53.20 70.00
## 267 255000 52.72 80.00
## 268 300000 55.03 93.40
## 270 150000 60.59 62.00
## 271 300000 72.29 75.00
## 273 270000 59.71 49.70
## 275 180000 62.72 57.63
## 276 285000 66.06 75.20
## 278 500000 66.46 75.00
## 279 250000 65.52 53.04
## 283 240000 70.10 88.00
## 286 240000 52.38 63.00
## 292 476000 66.39 80.00
## 293 290000 66.04 63.79
## 294 690000 72.97 95.50
## 295 300000 52.64 84.00
## 296 250000 64.79 49.00
## 297 162000 59.32 67.00
## 300 500000 66.23 64.00
## 302 220000 57.90 55.00
## 303 270000 58.67 76.20
## 304 650000 70.81 89.00
## 305 350000 68.07 73.00
## 306 300000 62.00 44.20
## 308 265000 56.60 57.00
## 309 180000 54.04 35.00
## 311 300000 64.28 62.00
## 313 300000 68.68 74.00
## 316 240000 54.12 0.00
## 319 276000 61.82 60.00
## 323 252000 71.43 82.00
## 325 280000 64.86 95.00
## 327 350000 66.63 60.00
## 333 264000 61.01 72.00
## 334 270000 57.34 93.40
## 335 300000 56.63 80.00
## 337 275000 58.95 84.00
## 339 300000 54.50 85.00
## 340 250000 54.48 78.00
## 341 260000 69.71 59.32
## 342 185000 71.96 88.00
## 343 216000 63.91 79.00
## 345 265000 55.80 73.00
## 346 300000 52.81 87.55
## 347 325000 56.12 84.00
## 348 267000 53.37 83.00
## 351 240000 60.11 61.28
## 353 260000 58.30 66.00
## 354 240000 69.12 63.00
## 356 250000 56.98 63.00
## 357 180000 63.42 60.00
## 359 210000 67.69 80.00
## 360 250000 52.64 48.00
## 361 250000 56.81 62.00
## 362 426000 60.39 26.53
## 363 270000 60.04 98.00
## 365 300000 71.55 88.56
## 366 132000 56.45 64.00
## 367 144000 62.92 92.66
## 369 216000 56.49 67.00
## 370 400000 74.49 91.00
## 371 275000 53.62 74.00
## 372 295000 69.72 59.00
## 373 360000 65.80 73.00
## 374 204000 60.23 70.00
## 378 350000 66.22 66.00
## 380 300000 77.30 96.16
## 381 180000 53.19 0.00
## 385 252000 61.00 0.00
## 387 162000 58.63 34.53
## 388 450000 59.50 50.53
## 389 240000 61.63 60.00
## 390 300000 70.17 77.00Q16 Draw a Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET.
library(car)
library(psych)
scatterplotMatrix(formula = ~ Salary + Percent_MBA + Percentile_ET, data = placedET)