setwd("C:/Users/jaya/downloads")
dean.df<-read.csv(paste("Data - Deans Dilemma.csv",sep=""))
View(dean.df)
#2 Summary Statistics of the data
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
# 3a. calculate the median salary of all the students in the data sample
library(psych)
median(dean.df$Salary)
## [1] 240000
# 3b. the percentage of students who were placed, correct to 2 decimal places.
placedTable <- table(dean.df$Placement)
placedTable
##
## Not Placed Placed
## 79 312
prop.table(placedTable)*100
##
## Not Placed Placed
## 20.2046 79.7954
# 3c. to create a dataframe called placed.
subset(dean.df,Placement_B==1,select =c("Placement", "SlNo"))
## Placement SlNo
## 1 Placed 1
## 2 Placed 2
## 3 Placed 3
## 4 Placed 4
## 5 Placed 5
## 6 Placed 6
## 7 Placed 7
## 8 Placed 8
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placed<-subset(dean.df,Placement_B==1,select =c("Placement", "SlNo"))
View(placed)
# 3d. median salary of students who were placed.
placedsalary<-subset(dean.df,Placement_B==1,select =c("Placement", "SlNo", "Salary"))
View(placedsalary)
median(placedsalary$Salary)
## [1] 260000
# 3e. mean salary of males and females, who were placed.
new.df<-subset(dean.df,Placement_B==1,select =c("Placement", "SlNo", "Salary", "Gender"))
new.df
## Placement SlNo Salary Gender
## 1 Placed 1 270000 M
## 2 Placed 2 200000 M
## 3 Placed 3 240000 M
## 4 Placed 4 250000 M
## 5 Placed 5 180000 M
## 6 Placed 6 300000 M
## 7 Placed 7 260000 F
## 8 Placed 8 235000 M
## 9 Placed 9 425000 M
## 10 Placed 10 240000 F
## 12 Placed 12 250000 M
## 13 Placed 13 180000 M
## 14 Placed 14 428000 M
## 15 Placed 15 450000 M
## 17 Placed 17 300000 M
## 18 Placed 18 240000 M
## 19 Placed 19 252000 M
## 21 Placed 21 280000 M
## 22 Placed 22 231000 M
## 23 Placed 23 224000 M
## 24 Placed 24 120000 M
## 25 Placed 25 260000 M
## 26 Placed 26 300000 M
## 27 Placed 27 120000 M
## 28 Placed 28 120000 M
## 29 Placed 29 250000 M
## 30 Placed 30 180000 F
## 31 Placed 31 218000 F
## 32 Placed 32 360000 M
## 33 Placed 33 150000 M
## 34 Placed 34 250000 F
## 35 Placed 35 200000 F
## 36 Placed 36 300000 M
## 37 Placed 37 330000 M
## 38 Placed 38 265000 M
## 39 Placed 39 340000 M
## 41 Placed 41 177600 F
## 44 Placed 44 236000 M
## 45 Placed 45 265000 M
## 46 Placed 46 200000 M
## 47 Placed 47 393000 F
## 48 Placed 48 360000 F
## 49 Placed 49 300000 F
## 50 Placed 50 250000 M
## 51 Placed 51 360000 M
## 52 Placed 52 180000 F
## 53 Placed 53 180000 F
## 54 Placed 54 270000 M
## 55 Placed 55 240000 M
## 56 Placed 56 300000 M
## 57 Placed 57 265000 M
## 58 Placed 58 350000 M
## 60 Placed 60 250000 F
## 61 Placed 61 180000 F
## 62 Placed 62 278000 F
## 63 Placed 63 150000 M
## 65 Placed 65 260000 F
## 66 Placed 66 180000 M
## 67 Placed 67 300000 F
## 69 Placed 69 400000 M
## 70 Placed 70 320000 F
## 71 Placed 71 240000 F
## 72 Placed 72 411000 M
## 73 Placed 73 287000 F
## 74 Placed 74 198000 F
## 76 Placed 76 300000 M
## 77 Placed 77 200000 F
## 78 Placed 78 180000 F
## 80 Placed 80 204000 M
## 81 Placed 81 250000 M
## 83 Placed 83 200000 F
## 84 Placed 84 275000 M
## 85 Placed 85 192000 M
## 86 Placed 86 240000 F
## 87 Placed 87 300000 F
## 90 Placed 90 450000 M
## 91 Placed 91 216000 F
## 92 Placed 92 220000 M
## 93 Placed 93 216000 M
## 95 Placed 95 300000 M
## 96 Placed 96 240000 M
## 97 Placed 97 360000 M
## 99 Placed 99 268000 M
## 101 Placed 101 265000 M
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## 103 Placed 103 240000 M
## 104 Placed 104 300000 M
## 105 Placed 105 240000 F
## 106 Placed 106 180000 M
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## 108 Placed 108 400000 M
## 110 Placed 110 180000 M
## 111 Placed 111 250000 M
## 113 Placed 113 295000 F
## 114 Placed 114 180000 M
## 115 Placed 115 300000 F
## 116 Placed 116 240000 M
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## 118 Placed 118 250000 M
## 119 Placed 119 275000 M
## 120 Placed 120 275000 M
## 121 Placed 121 150000 F
## 122 Placed 122 275000 M
## 123 Placed 123 300000 M
## 124 Placed 124 240000 M
## 125 Placed 125 336000 M
## 126 Placed 126 360000 M
## 127 Placed 127 280000 M
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## 131 Placed 131 240000 M
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## 133 Placed 133 336000 F
## 134 Placed 134 218000 M
## 135 Placed 135 216000 M
## 136 Placed 136 336000 M
## 137 Placed 137 190000 F
## 138 Placed 138 230000 F
## 139 Placed 139 390000 M
## 140 Placed 140 500000 M
## 141 Placed 141 270000 M
## 142 Placed 142 150000 F
## 143 Placed 143 240000 F
## 145 Placed 145 276000 F
## 146 Placed 146 300000 M
## 147 Placed 147 168000 M
## 148 Placed 148 300000 M
## 150 Placed 150 270000 M
## 151 Placed 151 360000 M
## 152 Placed 152 300000 M
## 153 Placed 153 400000 F
## 154 Placed 154 220000 M
## 155 Placed 155 180000 M
## 156 Placed 156 180000 M
## 157 Placed 157 210000 F
## 158 Placed 158 210000 F
## 159 Placed 159 300000 F
## 160 Placed 160 290000 F
## 161 Placed 161 180000 M
## 163 Placed 163 230000 F
## 164 Placed 164 282000 M
## 165 Placed 165 260000 M
## 167 Placed 167 180000 M
## 168 Placed 168 260000 M
## 169 Placed 169 400000 M
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## 171 Placed 171 144000 M
## 172 Placed 172 300000 F
## 173 Placed 173 150000 F
## 174 Placed 174 220000 F
## 177 Placed 177 380000 M
## 178 Placed 178 290000 M
## 179 Placed 179 300000 F
## 180 Placed 180 252000 F
## 181 Placed 181 280000 M
## 182 Placed 182 240000 M
## 183 Placed 183 360000 M
## 185 Placed 185 180000 M
## 186 Placed 186 450000 F
## 187 Placed 187 200000 M
## 188 Placed 188 300000 M
## 189 Placed 189 350000 M
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## 192 Placed 192 250000 M
## 193 Placed 193 250000 F
## 195 Placed 195 250000 M
## 196 Placed 196 280000 F
## 197 Placed 197 250000 M
## 198 Placed 198 216000 F
## 199 Placed 199 204000 M
## 200 Placed 200 300000 M
## 201 Placed 201 240000 M
## 202 Placed 202 276000 M
## 203 Placed 203 940000 M
## 205 Placed 205 250000 F
## 206 Placed 206 300000 M
## 207 Placed 207 180000 F
## 208 Placed 208 236000 F
## 209 Placed 209 240000 M
## 210 Placed 210 250000 M
## 211 Placed 211 350000 F
## 212 Placed 212 210000 M
## 213 Placed 213 210000 F
## 214 Placed 214 250000 F
## 215 Placed 215 400000 M
## 216 Placed 216 300000 M
## 217 Placed 217 480000 M
## 218 Placed 218 250000 M
## 219 Placed 219 320000 M
## 221 Placed 221 385000 M
## 222 Placed 222 360000 F
## 223 Placed 223 300000 M
## 224 Placed 224 375000 F
## 225 Placed 225 250000 M
## 227 Placed 227 250000 F
## 228 Placed 228 275000 F
## 229 Placed 229 200000 F
## 230 Placed 230 150000 F
## 232 Placed 232 300000 M
## 233 Placed 233 225000 M
## 234 Placed 234 120000 F
## 235 Placed 235 250000 F
## 237 Placed 237 220000 M
## 238 Placed 238 265000 M
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## 242 Placed 242 260000 M
## 243 Placed 243 300000 M
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## 247 Placed 247 263000 M
## 249 Placed 249 400000 M
## 250 Placed 250 233000 M
## 251 Placed 251 300000 M
## 253 Placed 253 240000 F
## 254 Placed 254 180000 M
## 255 Placed 255 350000 M
## 256 Placed 256 198000 M
## 257 Placed 257 250000 F
## 259 Placed 259 690000 M
## 260 Placed 260 270000 M
## 261 Placed 261 240000 F
## 262 Placed 262 300000 M
## 263 Placed 263 340000 M
## 264 Placed 264 250000 M
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## 267 Placed 267 255000 M
## 268 Placed 268 300000 M
## 270 Placed 270 150000 M
## 271 Placed 271 300000 M
## 273 Placed 273 270000 M
## 274 Placed 274 240000 F
## 275 Placed 275 180000 M
## 276 Placed 276 285000 M
## 277 Placed 277 400000 M
## 278 Placed 278 500000 M
## 279 Placed 279 250000 F
## 280 Placed 280 300000 M
## 281 Placed 281 240000 F
## 282 Placed 282 300000 M
## 283 Placed 283 240000 M
## 286 Placed 286 240000 M
## 292 Placed 292 476000 M
## 293 Placed 293 290000 M
## 294 Placed 294 690000 M
## 295 Placed 295 300000 M
## 296 Placed 296 250000 M
## 297 Placed 297 162000 F
## 299 Placed 299 260000 F
## 300 Placed 300 500000 M
## 302 Placed 302 220000 F
## 303 Placed 303 270000 M
## 304 Placed 304 650000 F
## 305 Placed 305 350000 M
## 306 Placed 306 300000 M
## 308 Placed 308 265000 M
## 309 Placed 309 180000 M
## 311 Placed 311 300000 F
## 312 Placed 312 300000 F
## 313 Placed 313 300000 F
## 314 Placed 314 220000 F
## 316 Placed 316 240000 M
## 319 Placed 319 276000 M
## 320 Placed 320 250000 M
## 321 Placed 321 180000 F
## 323 Placed 323 252000 F
## 325 Placed 325 280000 M
## 327 Placed 327 350000 F
## 331 Placed 331 216000 F
## 333 Placed 333 264000 M
## 334 Placed 334 270000 M
## 335 Placed 335 300000 F
## 337 Placed 337 275000 M
## 339 Placed 339 300000 M
## 340 Placed 340 250000 M
## 341 Placed 341 260000 F
## 342 Placed 342 185000 F
## 343 Placed 343 216000 F
## 345 Placed 345 265000 M
## 346 Placed 346 300000 M
## 347 Placed 347 325000 M
## 348 Placed 348 267000 M
## 349 Placed 349 264000 F
## 351 Placed 351 240000 M
## 353 Placed 353 260000 M
## 354 Placed 354 240000 F
## 356 Placed 356 250000 M
## 357 Placed 357 180000 F
## 358 Placed 358 366000 F
## 359 Placed 359 210000 F
## 360 Placed 360 250000 M
## 361 Placed 361 250000 M
## 362 Placed 362 426000 M
## 363 Placed 363 270000 M
## 365 Placed 365 300000 M
## 366 Placed 366 132000 M
## 367 Placed 367 144000 F
## 368 Placed 368 220000 M
## 369 Placed 369 216000 M
## 370 Placed 370 400000 M
## 371 Placed 371 275000 M
## 372 Placed 372 295000 M
## 373 Placed 373 360000 M
## 374 Placed 374 204000 F
## 378 Placed 378 350000 M
## 380 Placed 380 300000 F
## 381 Placed 381 180000 M
## 385 Placed 385 252000 M
## 387 Placed 387 162000 M
## 388 Placed 388 450000 M
## 389 Placed 389 240000 M
## 390 Placed 390 300000 F
surviversByClass <- xtabs(~ Salary+Gender, data=new.df)
surviversByClass
## Gender
## Salary F M
## 120000 1 4
## 132000 0 1
## 144000 1 1
## 150000 4 3
## 156000 0 1
## 162000 1 1
## 168000 0 1
## 177600 1 0
## 180000 8 16
## 185000 1 0
## 190000 1 0
## 192000 0 1
## 198000 1 1
## 200000 4 3
## 204000 1 3
## 210000 4 1
## 216000 4 3
## 218000 1 1
## 220000 3 4
## 224000 0 1
## 225000 0 1
## 230000 2 0
## 231000 0 1
## 233000 0 1
## 235000 0 1
## 236000 1 1
## 240000 10 18
## 250000 9 20
## 252000 2 2
## 255000 0 1
## 260000 4 6
## 263000 0 1
## 264000 1 1
## 265000 0 8
## 267000 0 1
## 268000 0 1
## 270000 0 9
## 275000 1 6
## 276000 1 2
## 278000 1 0
## 280000 1 4
## 282000 0 1
## 285000 0 1
## 287000 1 0
## 290000 1 2
## 295000 1 1
## 300000 13 30
## 320000 1 1
## 325000 0 2
## 330000 0 1
## 336000 1 2
## 340000 0 2
## 350000 2 5
## 360000 2 7
## 366000 1 0
## 375000 1 0
## 380000 0 1
## 385000 0 1
## 390000 0 2
## 393000 1 0
## 400000 1 7
## 411000 0 1
## 420000 0 1
## 425000 0 1
## 426000 0 1
## 428000 0 1
## 450000 1 3
## 476000 0 1
## 480000 0 1
## 500000 0 3
## 530000 0 1
## 550000 0 1
## 650000 1 0
## 690000 0 2
## 940000 0 1
mean(surviversByClass)
## [1] 2.08
# 3f. histogram showing a breakup of the MBA performance of the students who were placed
library(lattice)
histogram(~SlNo | Placement, data=placed)
# 3g. dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA.
notplaced<-subset(dean.df,Placement_B==0,select =c("Placement", "SlNo"))
View(notplaced)
# 3h. histograms side-by-side, of Placed and Not Placed students, as follows:
library(lattice)
histogram(~SlNo | Placement, data=dean.df, layout=c(2,1))
# 3i. two boxplots comparing salaries of males and females who were placed, as follows:
library(lattice)
bwplot(Gender ~ Salary, data=new.df, xlab = "salary" )
# 3j. dataframe called placedET, students in the MBA and gave some MBA entrance test
ET<-subset(dean.df,S.TEST==1, select =c("Placement", "SlNo", "Entrance_Test","Salary", "Percent_MBA", "Percentile_ET", "Placement_B"))
placedET<-subset(ET,Placement_B==1, select =c("Placement", "SlNo", "Entrance_Test","Salary", "Percent_MBA", "Percentile_ET"))
View(placedET)
# 3k Scatter Plot Matrix for Salary, Percent_MBA, Percentile_ET
####i was getting error by using scatterplotMatix###
#library(car)
#scatterplotMatrix(formula= ~ Salary + Percent_MBA + Percentile_ET, cex=0.6, data(placedET), diagonal="hisogram")
plot(placedET[4:6]) #so solved it this way.
