store.df <- read.csv(paste("Data - Deans Dilemma.csv" , sep = ""))
library(psych)
describe(store.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
store.df <- read.csv(paste("Data - Deans Dilemma.csv" , sep = ""))
median(store.df$Salary)
## [1] 240000
percent <- length(store.df$Placement[store.df$Placement == "Placed"]) / length(store.df$Placement) * 100
options(digits = 4)
percent
## [1] 79.8
placed <- subset(store.df, Placement == "Placed" , select = c(Percent_MBA , Gender , Salary))
placed
## Percent_MBA Gender Salary
## 1 58.80 M 270000
## 2 66.28 M 200000
## 3 52.91 M 240000
## 4 57.80 M 250000
## 5 59.43 M 180000
## 6 56.81 M 300000
## 7 59.80 F 260000
## 8 57.23 M 235000
## 9 55.50 M 425000
## 10 63.83 F 240000
## 12 54.01 M 250000
## 13 51.58 M 180000
## 14 66.92 M 428000
## 15 58.21 M 450000
## 17 58.94 M 300000
## 18 54.78 M 240000
## 19 62.14 M 252000
## 21 63.26 M 280000
## 22 61.29 M 231000
## 23 62.51 M 224000
## 24 52.21 M 120000
## 25 60.85 M 260000
## 26 60.77 M 300000
## 27 51.75 M 120000
## 28 58.56 M 120000
## 29 63.70 M 250000
## 30 65.04 F 180000
## 31 68.63 F 218000
## 32 57.68 M 360000
## 33 54.96 M 150000
## 34 64.19 F 250000
## 35 64.66 F 200000
## 36 62.54 M 300000
## 37 52.41 M 330000
## 38 56.61 M 265000
## 39 61.83 M 340000
## 41 64.08 F 177600
## 44 77.89 M 236000
## 45 56.70 M 265000
## 46 57.74 M 200000
## 47 69.06 F 393000
## 48 68.81 F 360000
## 49 63.62 F 300000
## 50 53.42 M 250000
## 51 74.01 M 360000
## 52 65.33 F 180000
## 53 62.80 F 180000
## 54 58.53 M 270000
## 55 57.55 M 240000
## 56 60.76 M 300000
## 57 57.69 M 265000
## 58 64.15 M 350000
## 60 56.70 F 250000
## 61 58.32 F 180000
## 62 62.21 F 278000
## 63 57.61 M 150000
## 65 72.78 F 260000
## 66 62.77 M 180000
## 67 62.74 F 300000
## 69 68.85 M 400000
## 70 55.47 F 320000
## 71 56.86 F 240000
## 72 62.56 M 411000
## 73 66.72 F 287000
## 74 69.76 F 198000
## 76 62.90 M 300000
## 77 69.70 F 200000
## 78 66.53 F 180000
## 80 54.55 M 204000
## 81 62.46 M 250000
## 83 62.98 F 200000
## 84 62.27 M 275000
## 85 62.65 M 192000
## 86 57.83 F 240000
## 87 60.91 F 300000
## 90 71.04 M 450000
## 91 65.56 F 216000
## 92 52.71 M 220000
## 93 55.10 M 216000
## 95 67.31 M 300000
## 96 66.88 M 240000
## 97 63.59 M 360000
## 99 57.99 M 268000
## 101 56.66 M 265000
## 102 57.24 M 260000
## 103 67.53 M 240000
## 104 62.48 M 300000
## 105 59.69 F 240000
## 106 52.82 M 180000
## 107 64.75 M 240000
## 108 57.76 M 400000
## 110 52.43 M 180000
## 111 76.72 M 250000
## 113 69.35 F 295000
## 114 59.50 M 180000
## 115 62.89 F 300000
## 116 58.78 M 240000
## 117 57.10 M 120000
## 118 59.10 M 250000
## 119 58.46 M 275000
## 120 60.99 M 275000
## 121 59.24 F 150000
## 122 68.07 M 275000
## 123 60.03 M 300000
## 124 58.75 M 240000
## 125 69.81 M 336000
## 126 65.45 M 360000
## 127 62.40 M 280000
## 129 60.43 M 325000
## 130 60.76 M 204000
## 131 66.94 M 240000
## 132 68.53 M 240000
## 133 61.41 F 336000
## 134 59.75 M 218000
## 135 55.02 M 216000
## 136 67.20 M 336000
## 137 67.00 F 190000
## 138 64.27 F 230000
## 139 51.24 M 390000
## 140 57.65 M 500000
## 141 59.42 M 270000
## 142 67.99 F 150000
## 143 62.35 F 240000
## 145 62.01 F 276000
## 146 70.20 M 300000
## 147 60.44 M 168000
## 148 66.69 M 300000
## 150 59.81 M 270000
## 151 55.60 M 360000
## 152 62.00 M 300000
## 153 76.18 F 400000
## 154 57.03 M 220000
## 155 59.08 M 180000
## 156 58.85 M 180000
## 157 64.36 F 210000
## 158 62.36 F 210000
## 159 68.03 F 300000
## 160 66.86 F 290000
## 161 62.79 M 180000
## 163 59.47 F 230000
## 164 64.63 M 282000
## 165 53.57 M 260000
## 167 66.50 M 180000
## 168 54.97 M 260000
## 169 56.51 M 400000
## 170 62.16 M 420000
## 171 54.35 M 144000
## 172 64.44 F 300000
## 173 69.03 F 150000
## 174 57.31 F 220000
## 177 60.44 M 380000
## 178 59.99 M 290000
## 179 61.31 F 300000
## 180 55.42 F 252000
## 181 63.39 M 280000
## 182 65.83 M 240000
## 183 58.23 M 360000
## 185 65.69 M 180000
## 186 67.83 F 450000
## 187 73.52 M 200000
## 188 58.31 M 300000
## 189 53.37 M 350000
## 190 56.11 M 550000
## 192 63.36 M 250000
## 193 54.80 F 250000
## 195 53.94 M 250000
## 196 63.08 F 280000
## 197 55.01 M 250000
## 198 60.50 F 216000
## 199 52.42 M 204000
## 200 70.85 M 300000
## 201 67.05 M 240000
## 202 70.48 M 276000
## 203 64.34 M 940000
## 205 71.49 F 250000
## 206 59.99 M 300000
## 207 57.98 F 180000
## 208 71.00 F 236000
## 209 56.70 M 240000
## 210 61.26 M 250000
## 211 73.33 F 350000
## 212 59.50 M 210000
## 213 68.20 F 210000
## 214 58.40 F 250000
## 215 76.26 M 400000
## 216 70.71 M 300000
## 217 61.79 M 480000
## 218 68.55 M 250000
## 219 67.54 M 320000
## 221 69.94 M 385000
## 222 60.78 F 360000
## 223 53.49 M 300000
## 224 73.87 F 375000
## 225 60.98 M 250000
## 227 67.13 F 250000
## 228 58.73 F 275000
## 229 65.63 F 200000
## 230 61.58 F 150000
## 232 60.95 M 300000
## 233 60.41 M 225000
## 234 60.00 F 120000
## 235 71.77 F 250000
## 237 54.43 M 220000
## 238 57.24 M 265000
## 239 56.94 M 265000
## 242 61.29 M 260000
## 243 60.39 M 300000
## 244 51.73 M 180000
## 245 59.54 M 530000
## 246 56.75 M 156000
## 247 58.95 M 263000
## 249 63.23 M 400000
## 250 55.14 M 233000
## 251 62.28 M 300000
## 253 64.08 F 240000
## 254 58.54 M 180000
## 255 62.89 M 350000
## 256 55.67 M 198000
## 257 68.55 F 250000
## 259 61.30 M 690000
## 260 58.87 M 270000
## 261 65.25 F 240000
## 262 69.08 M 300000
## 263 62.48 M 340000
## 264 53.20 M 250000
## 265 59.84 M 390000
## 267 52.72 M 255000
## 268 55.03 M 300000
## 270 60.59 M 150000
## 271 72.29 M 300000
## 273 59.71 M 270000
## 274 53.47 F 240000
## 275 62.72 M 180000
## 276 66.06 M 285000
## 277 69.67 M 400000
## 278 66.46 M 500000
## 279 65.52 F 250000
## 280 56.78 M 300000
## 281 67.06 F 240000
## 282 71.86 M 300000
## 283 70.10 M 240000
## 286 52.38 M 240000
## 292 66.39 M 476000
## 293 66.04 M 290000
## 294 72.97 M 690000
## 295 52.64 M 300000
## 296 64.79 M 250000
## 297 59.32 F 162000
## 299 66.90 F 260000
## 300 66.23 M 500000
## 302 57.90 F 220000
## 303 58.67 M 270000
## 304 70.81 F 650000
## 305 68.07 M 350000
## 306 62.00 M 300000
## 308 56.60 M 265000
## 309 54.04 M 180000
## 311 64.28 F 300000
## 312 66.00 F 300000
## 313 68.68 F 300000
## 314 59.15 F 220000
## 316 54.12 M 240000
## 319 61.82 M 276000
## 320 66.28 M 250000
## 321 67.96 F 180000
## 323 71.43 F 252000
## 325 64.86 M 280000
## 327 66.63 F 350000
## 331 66.61 F 216000
## 333 61.01 M 264000
## 334 57.34 M 270000
## 335 56.63 F 300000
## 337 58.95 M 275000
## 339 54.50 M 300000
## 340 54.48 M 250000
## 341 69.71 F 260000
## 342 71.96 F 185000
## 343 63.91 F 216000
## 345 55.80 M 265000
## 346 52.81 M 300000
## 347 56.12 M 325000
## 348 53.37 M 267000
## 349 62.95 F 264000
## 351 60.11 M 240000
## 353 58.30 M 260000
## 354 69.12 F 240000
## 356 56.98 M 250000
## 357 63.42 F 180000
## 358 69.52 F 366000
## 359 67.69 F 210000
## 360 52.64 M 250000
## 361 56.81 M 250000
## 362 60.39 M 426000
## 363 60.04 M 270000
## 365 71.55 M 300000
## 366 56.45 M 132000
## 367 62.92 F 144000
## 368 55.40 M 220000
## 369 56.49 M 216000
## 370 74.49 M 400000
## 371 53.62 M 275000
## 372 69.72 M 295000
## 373 65.80 M 360000
## 374 60.23 F 204000
## 378 66.22 M 350000
## 380 77.30 F 300000
## 381 53.19 M 180000
## 385 61.00 M 252000
## 387 58.63 M 162000
## 388 59.50 M 450000
## 389 61.63 M 240000
## 390 70.17 F 300000
median(store.df$Salary[store.df$Placement == "Placed"] , na.rm = FALSE)
## [1] 260000
agg.data <- aggregate(Salary ~ Gender + Placement , data = store.df , mean)
agg.data
## Gender Placement Salary
## 1 F Not Placed 0
## 2 M Not Placed 0
## 3 F Placed 253068
## 4 M Placed 284242
hist(placed$Percent_MBA , main = "MBA Performance of placed students" , xlab = "MBA Percentage" , ylab = "Count" , xlim = c(50,80) , ylim = c(0,150) , col = "grey" , breaks = c(50,seq(60,80,10)))
notplaced <- subset(store.df , Placement == "Not Placed" , select = c(Percent_MBA , Gender , Salary))
notplaced
## Percent_MBA Gender Salary
## 11 69.78 F 0
## 16 53.29 F 0
## 20 54.65 M 0
## 40 67.28 F 0
## 42 51.75 F 0
## 43 56.34 M 0
## 59 51.29 M 0
## 64 52.56 M 0
## 68 51.45 M 0
## 75 51.21 M 0
## 79 71.63 F 0
## 82 56.11 F 0
## 88 56.19 M 0
## 89 65.49 F 0
## 94 61.31 M 0
## 98 60.29 M 0
## 100 56.45 F 0
## 109 72.00 M 0
## 112 54.76 F 0
## 128 71.15 F 0
## 144 67.13 F 0
## 149 55.83 M 0
## 162 58.00 M 0
## 166 55.41 M 0
## 175 59.47 M 0
## 176 64.95 F 0
## 184 55.30 M 0
## 191 56.09 M 0
## 194 60.64 M 0
## 204 58.81 M 0
## 220 64.15 M 0
## 226 62.29 F 0
## 231 62.83 F 0
## 236 57.32 F 0
## 240 61.90 M 0
## 241 61.22 M 0
## 248 58.52 M 0
## 252 52.32 M 0
## 258 55.87 M 0
## 266 65.99 M 0
## 269 61.87 M 0
## 272 65.13 M 0
## 284 74.56 F 0
## 285 54.99 M 0
## 287 75.71 M 0
## 288 57.16 M 0
## 289 58.79 F 0
## 290 65.48 M 0
## 291 69.28 F 0
## 298 67.44 F 0
## 301 60.69 M 0
## 307 72.14 F 0
## 310 60.02 M 0
## 315 63.83 F 0
## 317 59.81 M 0
## 318 61.66 F 0
## 322 57.29 F 0
## 324 62.93 F 0
## 326 56.13 M 0
## 328 66.94 F 0
## 329 63.94 M 0
## 330 62.50 F 0
## 332 66.18 M 0
## 336 64.74 M 0
## 338 65.28 M 0
## 344 63.53 F 0
## 350 58.44 M 0
## 352 72.21 F 0
## 355 51.48 M 0
## 364 53.39 M 0
## 375 62.42 F 0
## 376 60.22 M 0
## 377 52.36 M 0
## 379 56.00 M 0
## 382 50.83 M 0
## 383 56.81 F 0
## 384 59.14 M 0
## 386 67.94 M 0
## 391 60.36 M 0
par(mfrow = c(1,2))
hist(placed$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)))
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)))
par(mfrow = c(1,1))
library(lattice)
bwplot(Gender ~ Salary , data = placed , horizontal = TRUE , xlab = "Salary" , ylab = "Gender" , main = "Comparison of Salaries of Males and Females")
placedET <- subset(store.df , 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
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## 250 233000 55.14 85.00
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## 256 198000 55.67 40.00
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## 267 255000 52.72 80.00
## 268 300000 55.03 93.40
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## 304 650000 70.81 89.00
## 305 350000 68.07 73.00
## 306 300000 62.00 44.20
## 308 265000 56.60 57.00
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## 335 300000 56.63 80.00
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## 342 185000 71.96 88.00
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## 348 267000 53.37 83.00
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## 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
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## 371 275000 53.62 74.00
## 372 295000 69.72 59.00
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## 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 77.00
library(psych)
library(car)
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
scatterplotMatrix(formula = ~ Salary + Percent_MBA + Percentile_ET, data = placedET)
