MBA STARTING SALARY
Kumar Saurabh
27 December 2017
Reading data:
mba.df <- read.csv(paste("file:///C:/Program Files/RStudio/Data/MBA Starting Salaries Data.csv", sep=""))
View(mba.df)
str(mba.df)## 'data.frame': 274 obs. of 13 variables:
## $ age : int 23 24 24 24 24 24 25 25 25 25 ...
## $ sex : int 2 1 1 1 2 1 1 2 1 1 ...
## $ gmat_tot: int 620 610 670 570 710 640 610 650 630 680 ...
## $ gmat_qpc: int 77 90 99 56 93 82 89 88 79 99 ...
## $ gmat_vpc: int 87 71 78 81 98 89 74 89 91 81 ...
## $ gmat_tpc: int 87 87 95 75 98 91 87 92 89 96 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.6 3.9 3.4 3.3 3.3 3.45 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.75 3.5 3.75 3.25 3.67 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 2 2 2 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 999 0 0 0 999 998 ...
## $ satis : int 7 6 6 7 5 6 5 6 4 998 ...Different data frames
- Those who disclosed their salary and satisfaction
mba1.df <- mba.df[which(mba.df$salary!= 998 & mba.df$salary!= 999 & mba.df$satis!= 998), ]
mba1.df## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 1 23 2 620 77 87 87 3.40 3.00 1
## 2 24 1 610 90 71 87 3.50 4.00 1
## 3 24 1 670 99 78 95 3.30 3.25 1
## 4 24 1 570 56 81 75 3.30 2.67 1
## 6 24 1 640 82 89 91 3.90 3.75 1
## 7 25 1 610 89 74 87 3.40 3.50 1
## 8 25 2 650 88 89 92 3.30 3.75 1
## 22 27 1 740 99 96 99 3.50 3.50 1
## 23 27 1 750 99 98 99 3.40 3.50 1
## 24 28 2 540 75 50 65 3.60 4.00 1
## 25 29 1 580 56 87 78 3.64 3.33 1
## 27 31 2 560 60 78 72 3.30 3.75 1
## 28 32 1 760 99 99 99 3.40 3.00 1
## 29 32 1 640 79 91 91 3.60 3.75 1
## 31 34 2 620 75 89 87 3.30 3.00 1
## 32 37 2 560 43 87 72 3.40 3.50 1
## 33 42 2 650 75 98 93 3.38 3.00 1
## 34 48 1 590 84 62 81 3.80 4.00 1
## 35 22 2 660 90 92 94 3.50 3.75 1
## 36 27 2 700 94 98 98 3.30 3.25 1
## 37 25 2 680 87 96 96 3.50 2.67 1
## 38 25 2 650 82 91 93 3.40 3.25 1
## 39 27 1 710 96 96 98 3.30 3.50 1
## 40 28 2 620 52 98 87 3.40 3.75 1
## 41 24 1 670 84 96 95 3.30 3.25 1
## 42 25 2 560 52 81 72 3.30 3.50 1
## 43 25 2 530 50 62 61 3.60 3.67 1
## 44 25 1 650 79 93 93 3.30 3.50 1
## 45 26 2 590 56 89 81 3.30 3.25 1
## 46 23 2 650 93 81 93 3.40 3.00 1
## 47 24 1 560 81 50 71 3.40 3.67 1
## 48 27 1 610 72 84 86 3.30 3.50 1
## 49 25 1 650 95 84 93 3.30 3.00 1
## 50 25 1 550 74 50 68 3.50 3.50 1
## 51 26 1 570 68 74 75 3.80 3.50 1
## 52 26 1 580 79 71 78 3.45 3.50 1
## 53 30 1 600 60 91 83 3.30 3.25 1
## 54 31 1 570 72 71 75 3.60 3.50 1
## 55 30 1 620 60 96 87 3.50 3.00 1
## 56 30 2 680 96 87 96 3.70 3.60 1
## 57 27 1 630 93 75 91 3.30 3.25 1
## 58 25 1 600 82 74 83 3.50 3.25 1
## 59 28 2 640 89 81 91 3.60 3.50 1
## 60 39 1 600 72 81 83 3.60 3.50 1
## 61 27 1 570 95 33 75 3.70 4.00 1
## 62 27 1 710 95 98 98 3.60 3.50 1
## 63 33 1 620 72 89 87 3.50 3.50 1
## 64 27 1 600 67 84 83 3.50 3.00 1
## 65 28 1 700 95 95 98 3.80 4.00 1
## 66 30 1 600 77 81 84 3.50 3.25 1
## 67 30 2 670 87 95 95 3.30 3.25 1
## 68 40 1 630 71 95 91 4.00 0.00 1
## 69 25 1 700 98 93 98 3.60 3.75 1
## 70 22 1 600 95 54 83 3.00 3.00 2
## 71 23 1 640 89 87 92 3.00 3.00 2
## 72 24 1 550 73 63 69 3.10 3.00 2
## 73 24 1 570 82 58 75 3.09 3.50 2
## 74 24 1 620 82 84 87 3.10 3.50 2
## 75 25 2 570 61 81 76 3.00 3.25 2
## 76 25 1 660 94 84 94 3.27 3.75 2
## 77 25 1 680 94 92 97 3.17 3.50 2
## 88 26 2 560 64 71 72 3.20 3.25 2
## 89 26 1 560 87 41 72 3.00 3.00 2
## 90 26 1 530 68 54 62 3.09 3.17 2
## 92 27 1 720 99 95 99 3.10 3.25 2
## 93 27 1 590 60 87 81 3.00 2.75 2
## 97 28 1 620 81 90 89 3.20 3.00 2
## 98 28 2 610 85 78 86 3.10 3.00 2
## 100 29 1 660 94 87 94 3.00 3.00 2
## 102 29 1 510 57 50 55 3.27 3.40 2
## 103 29 2 640 90 84 92 3.20 3.00 2
## 104 29 1 610 91 62 86 3.10 3.67 2
## 106 29 1 580 79 67 78 3.00 3.25 2
## 107 30 1 680 97 87 96 3.00 3.00 2
## 109 32 2 610 64 89 86 3.25 0.00 2
## 110 35 1 540 43 78 65 3.20 3.25 2
## 111 35 1 630 66 95 90 3.08 3.25 2
## 112 36 2 530 48 71 62 3.00 2.50 2
## 113 36 1 650 87 89 93 3.00 3.20 2
## 114 43 1 630 82 87 89 3.10 3.00 2
## 115 26 2 670 87 95 95 3.10 3.33 2
## 116 25 2 620 89 74 87 3.10 3.50 2
## 117 31 1 540 60 62 65 3.10 3.00 2
## 118 25 1 670 95 89 95 3.20 3.50 2
## 119 25 1 610 87 71 86 3.27 3.25 2
## 120 24 1 560 52 81 72 3.20 3.25 2
## 121 24 1 500 78 30 52 3.00 2.75 2
## 122 23 1 590 72 81 81 3.20 3.25 2
## 123 24 1 570 82 58 75 3.20 3.25 2
## 124 26 2 570 93 37 75 3.00 2.75 2
## 125 28 2 580 83 58 79 3.10 3.00 2
## 126 24 2 580 72 71 78 3.00 3.25 2
## 127 31 1 560 68 67 72 3.09 3.00 2
## 128 25 2 620 89 74 87 3.10 3.50 2
## 129 27 1 620 97 63 88 3.20 3.00 2
## 130 28 1 560 75 58 72 3.20 3.25 2
## 131 26 1 680 84 96 96 3.20 3.25 2
## 132 27 1 620 81 87 89 3.00 3.00 2
## 133 34 1 550 72 58 69 3.00 3.00 2
## 134 26 1 600 84 67 83 3.09 3.50 2
## 135 29 1 670 91 93 95 3.10 3.00 2
## 136 24 1 620 84 81 87 3.00 3.25 2
## 137 27 1 630 72 95 89 3.20 3.00 2
## 138 26 1 650 89 87 93 3.20 3.25 2
## 139 24 1 620 88 74 87 3.10 3.00 2
## 140 23 1 720 95 98 99 2.80 2.50 3
## 141 24 2 640 94 78 92 2.90 3.25 3
## 142 24 1 710 96 97 99 2.80 2.75 3
## 143 24 1 670 94 89 96 2.70 3.00 3
## 144 24 2 710 97 97 99 2.80 3.00 3
## 146 24 1 600 89 62 83 2.90 3.00 3
## 147 24 2 640 96 71 91 2.70 2.50 3
## 150 25 1 550 72 58 69 2.90 3.00 3
## 151 25 1 710 99 91 98 2.90 3.25 3
## 159 26 1 560 56 81 72 2.80 3.25 3
## 160 26 1 540 52 71 65 2.70 2.75 3
## 162 26 2 570 48 89 75 2.82 2.50 3
## 163 26 1 610 82 81 86 2.90 2.75 3
## 164 27 1 650 89 84 93 2.90 3.00 3
## 165 27 2 550 66 63 69 2.90 3.00 3
## 167 27 1 610 97 45 86 2.70 2.50 3
## 168 27 2 630 82 89 89 2.70 3.25 3
## 169 27 2 560 61 74 73 2.80 3.25 3
## 180 29 1 590 92 58 81 2.80 2.75 3
## 182 32 1 550 52 78 71 2.70 2.75 3
## 183 34 1 610 79 81 86 2.80 3.00 3
## 184 34 1 610 82 78 86 2.70 3.00 3
## 185 43 1 480 49 41 45 2.90 3.25 3
## 186 23 2 520 43 67 58 2.90 2.75 3
## 187 27 1 620 87 74 87 2.70 2.75 3
## 188 25 1 580 78 67 80 2.90 3.25 3
## 189 25 1 630 75 93 89 2.70 2.50 3
## 190 25 1 610 89 74 87 2.70 2.75 3
## 191 29 2 560 64 71 72 2.90 3.00 3
## 192 27 1 620 79 87 88 2.90 2.75 3
## 193 28 1 580 72 71 78 2.80 3.00 3
## 194 24 2 670 83 98 96 2.90 3.25 3
## 195 25 2 560 39 91 72 2.90 3.00 3
## 196 25 2 580 72 71 78 2.80 3.25 3
## 197 27 1 680 97 90 97 2.90 2.75 3
## 198 28 1 610 89 67 86 2.70 3.00 3
## 199 29 1 710 93 98 99 2.90 3.25 3
## 200 24 1 710 99 92 99 2.90 3.00 3
## 201 25 2 630 84 87 89 2.80 2.75 3
## 202 24 2 600 89 67 85 2.80 3.00 3
## 203 29 1 660 91 90 95 2.80 3.00 3
## 204 30 1 670 83 97 96 2.80 2.75 3
## 205 24 1 580 89 54 78 2.91 2.83 3
## 206 29 1 680 79 99 96 2.90 3.00 3
## 207 32 1 660 83 95 94 2.90 3.50 3
## 208 28 1 570 56 84 75 2.90 3.00 3
## 209 24 1 680 96 87 97 2.80 2.75 3
## 213 25 1 730 98 96 99 2.40 2.75 4
## 218 25 1 700 99 87 98 2.00 2.00 4
## 219 26 1 660 93 87 95 2.60 2.00 4
## 220 26 1 450 28 46 34 2.10 2.00 4
## 222 26 1 600 75 78 83 2.20 2.25 4
## 227 27 2 560 59 74 73 2.40 2.50 4
## 229 27 1 630 93 78 91 2.10 2.50 4
## 230 27 1 580 84 58 78 2.70 2.75 4
## 232 27 1 670 89 91 95 3.60 3.25 4
## 233 27 1 580 74 70 78 3.40 3.25 4
## 234 28 1 560 74 67 73 3.60 3.60 4
## 236 28 1 710 94 98 99 3.40 3.75 4
## 237 28 1 570 69 71 0 2.30 2.50 4
## 238 29 1 530 35 81 62 3.30 2.75 4
## 241 29 1 670 91 91 95 3.30 3.25 4
## 242 29 1 630 99 50 89 2.90 3.25 4
## 243 29 2 680 89 96 96 2.80 3.00 4
## 244 30 1 650 88 92 93 3.45 3.83 4
## 250 31 1 570 75 62 75 2.80 3.00 4
## 253 32 1 510 79 22 54 2.30 2.25 4
## 254 35 1 570 72 71 75 3.30 4.00 4
## 255 39 2 700 89 98 98 3.30 3.25 4
## 256 24 2 560 55 78 71 3.50 3.25 4
## 257 23 1 660 81 98 95 2.50 3.00 4
## 258 25 2 720 96 98 99 3.50 3.60 4
## 259 26 1 620 78 87 89 2.40 2.00 4
## 260 26 2 630 85 81 90 2.90 3.25 4
## 261 27 1 650 89 89 93 2.40 2.25 4
## 262 25 1 660 99 71 95 3.40 3.25 4
## 263 25 1 610 83 81 86 2.40 2.75 4
## 264 26 1 600 87 62 83 2.50 2.50 4
## 265 24 1 570 75 62 75 2.30 2.50 4
## 266 24 2 600 77 78 84 2.60 3.00 4
## 267 26 2 650 91 84 93 2.60 3.00 4
## 268 29 1 630 72 95 89 2.60 2.50 4
## 269 26 1 630 96 71 91 2.60 2.75 4
## 270 31 1 530 75 45 62 2.40 2.75 4
## 271 23 1 580 64 81 78 2.20 2.00 4
## 272 25 1 540 79 45 65 2.60 2.50 4
## 273 26 1 550 72 58 69 2.60 2.75 4
## 274 40 2 500 60 45 51 2.50 2.75 4
## work_yrs frstlang salary satis
## 1 2 1 0 7
## 2 2 1 0 6
## 3 2 1 0 6
## 4 1 1 0 7
## 6 2 1 0 6
## 7 2 1 0 5
## 8 2 1 0 6
## 22 3 1 0 6
## 23 1 2 0 5
## 24 5 1 0 5
## 25 3 1 0 5
## 27 10 1 0 7
## 28 5 1 0 5
## 29 7 1 0 6
## 31 7 1 0 6
## 32 9 1 0 6
## 33 13 1 0 5
## 34 22 1 0 6
## 35 1 1 85000 5
## 36 2 1 85000 6
## 37 2 1 86000 5
## 38 3 1 88000 7
## 39 2 1 92000 6
## 40 5 1 93000 5
## 41 0 1 95000 4
## 42 1 1 95000 5
## 43 3 1 95000 3
## 44 1 1 96000 7
## 45 4 1 96000 5
## 46 2 1 100000 7
## 47 2 1 100000 6
## 48 6 1 100000 6
## 49 2 1 105000 7
## 50 3 1 105000 6
## 51 3 1 105000 6
## 52 2 1 105000 5
## 53 5 1 105000 6
## 54 6 1 105000 6
## 55 8 1 106000 7
## 56 6 1 106000 6
## 57 3 1 107500 5
## 58 3 1 108000 6
## 59 6 1 110000 5
## 60 16 1 112000 7
## 61 4 1 115000 5
## 62 1 1 115000 5
## 63 10 2 118000 7
## 64 3 1 120000 5
## 65 5 1 120000 5
## 66 5 1 120000 6
## 67 8 1 120000 6
## 68 15 1 146000 6
## 69 1 1 162000 5
## 70 1 1 0 5
## 71 2 1 0 7
## 72 0 2 0 5
## 73 2 1 0 6
## 74 1 1 0 5
## 75 3 1 0 4
## 76 2 1 0 5
## 77 2 1 0 6
## 88 3 1 0 6
## 89 3 1 0 6
## 90 4 2 0 5
## 92 5 1 0 5
## 93 3 1 0 6
## 97 4 1 0 6
## 98 5 1 0 6
## 100 1 1 0 6
## 102 5 1 0 5
## 103 3 1 0 5
## 104 7 1 0 5
## 106 4 1 0 6
## 107 4 1 0 5
## 109 11 1 0 7
## 110 8 1 0 5
## 111 12 1 0 5
## 112 7 1 0 5
## 113 18 1 0 6
## 114 16 1 0 5
## 115 1 1 82000 7
## 116 2 1 92000 5
## 117 8 1 93000 6
## 118 2 1 95000 6
## 119 3 1 95000 6
## 120 2 1 96000 7
## 121 2 1 96500 6
## 122 2 1 98000 6
## 123 2 1 98000 6
## 124 3 2 98000 5
## 125 5 2 99000 6
## 126 2 1 100000 5
## 127 4 1 100000 6
## 128 2 1 101000 5
## 129 3 1 103000 6
## 130 4 1 104000 5
## 131 3 1 105000 6
## 132 3 1 105000 5
## 133 16 1 105000 5
## 134 2 1 107000 5
## 135 6 1 112000 6
## 136 1 1 115000 6
## 137 4 1 115000 6
## 138 4 1 130000 7
## 139 2 1 145800 6
## 140 1 1 0 5
## 141 2 2 0 4
## 142 2 1 0 7
## 143 2 1 0 7
## 144 2 1 0 7
## 146 1 1 0 6
## 147 2 1 0 6
## 150 3 1 0 6
## 151 1 1 0 6
## 159 4 1 0 6
## 160 2 1 0 6
## 162 3 1 0 5
## 163 3 1 0 6
## 164 2 1 0 6
## 165 3 1 0 4
## 167 4 2 0 5
## 168 5 1 0 6
## 169 5 1 0 6
## 180 3 2 0 5
## 182 7 1 0 6
## 183 11 1 0 6
## 184 12 1 0 5
## 185 22 1 0 5
## 186 1 1 78256 5
## 187 3 1 88500 6
## 188 2 1 90000 7
## 189 2 1 90000 5
## 190 4 1 93000 6
## 191 5 1 95000 7
## 192 4 1 97000 7
## 193 3 1 97000 6
## 194 2 1 98000 7
## 195 2 1 98000 7
## 196 2 1 98000 6
## 197 2 2 98000 6
## 198 4 1 98000 7
## 199 7 1 98000 5
## 200 3 1 100000 6
## 201 2 1 100000 6
## 202 2 1 101000 6
## 203 8 1 101100 6
## 204 6 1 102500 5
## 205 2 1 105000 5
## 206 6 1 106000 6
## 207 2 2 107300 7
## 208 4 1 108000 6
## 209 2 1 112000 6
## 213 2 1 0 6
## 218 1 1 0 7
## 219 2 1 0 5
## 220 4 1 0 6
## 222 2 1 0 6
## 227 2 1 0 5
## 229 4 1 0 5
## 230 1 1 0 5
## 232 5 1 0 6
## 233 3 1 0 6
## 234 5 1 0 5
## 236 6 1 0 6
## 237 5 1 0 5
## 238 6 1 0 7
## 241 3 1 0 5
## 242 1 2 0 4
## 243 4 1 0 5
## 244 2 1 0 6
## 250 1 1 0 6
## 253 5 2 0 5
## 254 8 1 0 6
## 255 5 1 0 5
## 256 2 1 64000 7
## 257 2 1 77000 6
## 258 3 1 85000 6
## 259 2 1 85000 6
## 260 3 1 86000 5
## 261 5 1 90000 5
## 262 2 1 92000 7
## 263 2 1 95000 7
## 264 2 1 96000 6
## 265 2 1 98000 6
## 266 2 1 100000 6
## 267 2 1 100000 7
## 268 3 1 100400 7
## 269 3 1 101600 6
## 270 4 2 104000 6
## 271 2 1 105000 6
## 272 3 1 115000 5
## 273 3 1 126710 6
## 274 15 2 220000 6str(mba1.df)## 'data.frame': 193 obs. of 13 variables:
## $ age : int 23 24 24 24 24 25 25 27 27 28 ...
## $ sex : int 2 1 1 1 1 1 2 1 1 2 ...
## $ gmat_tot: int 620 610 670 570 640 610 650 740 750 540 ...
## $ gmat_qpc: int 77 90 99 56 82 89 88 99 99 75 ...
## $ gmat_vpc: int 87 71 78 81 89 74 89 96 98 50 ...
## $ gmat_tpc: int 87 87 95 75 91 87 92 99 99 65 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.9 3.4 3.3 3.5 3.4 3.6 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.5 3.75 3.5 3.5 4 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 3 1 5 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 0 0 0 0 0 0 ...
## $ satis : int 7 6 6 7 6 5 6 6 5 5 ...- Those who disclosed their salary
mba2.df <- mba.df[which(mba.df$salary!= 998 & mba.df$salary!= 999), ]
mba2.df## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 1 23 2 620 77 87 87 3.40 3.00 1
## 2 24 1 610 90 71 87 3.50 4.00 1
## 3 24 1 670 99 78 95 3.30 3.25 1
## 4 24 1 570 56 81 75 3.30 2.67 1
## 6 24 1 640 82 89 91 3.90 3.75 1
## 7 25 1 610 89 74 87 3.40 3.50 1
## 8 25 2 650 88 89 92 3.30 3.75 1
## 22 27 1 740 99 96 99 3.50 3.50 1
## 23 27 1 750 99 98 99 3.40 3.50 1
## 24 28 2 540 75 50 65 3.60 4.00 1
## 25 29 1 580 56 87 78 3.64 3.33 1
## 27 31 2 560 60 78 72 3.30 3.75 1
## 28 32 1 760 99 99 99 3.40 3.00 1
## 29 32 1 640 79 91 91 3.60 3.75 1
## 31 34 2 620 75 89 87 3.30 3.00 1
## 32 37 2 560 43 87 72 3.40 3.50 1
## 33 42 2 650 75 98 93 3.38 3.00 1
## 34 48 1 590 84 62 81 3.80 4.00 1
## 35 22 2 660 90 92 94 3.50 3.75 1
## 36 27 2 700 94 98 98 3.30 3.25 1
## 37 25 2 680 87 96 96 3.50 2.67 1
## 38 25 2 650 82 91 93 3.40 3.25 1
## 39 27 1 710 96 96 98 3.30 3.50 1
## 40 28 2 620 52 98 87 3.40 3.75 1
## 41 24 1 670 84 96 95 3.30 3.25 1
## 42 25 2 560 52 81 72 3.30 3.50 1
## 43 25 2 530 50 62 61 3.60 3.67 1
## 44 25 1 650 79 93 93 3.30 3.50 1
## 45 26 2 590 56 89 81 3.30 3.25 1
## 46 23 2 650 93 81 93 3.40 3.00 1
## 47 24 1 560 81 50 71 3.40 3.67 1
## 48 27 1 610 72 84 86 3.30 3.50 1
## 49 25 1 650 95 84 93 3.30 3.00 1
## 50 25 1 550 74 50 68 3.50 3.50 1
## 51 26 1 570 68 74 75 3.80 3.50 1
## 52 26 1 580 79 71 78 3.45 3.50 1
## 53 30 1 600 60 91 83 3.30 3.25 1
## 54 31 1 570 72 71 75 3.60 3.50 1
## 55 30 1 620 60 96 87 3.50 3.00 1
## 56 30 2 680 96 87 96 3.70 3.60 1
## 57 27 1 630 93 75 91 3.30 3.25 1
## 58 25 1 600 82 74 83 3.50 3.25 1
## 59 28 2 640 89 81 91 3.60 3.50 1
## 60 39 1 600 72 81 83 3.60 3.50 1
## 61 27 1 570 95 33 75 3.70 4.00 1
## 62 27 1 710 95 98 98 3.60 3.50 1
## 63 33 1 620 72 89 87 3.50 3.50 1
## 64 27 1 600 67 84 83 3.50 3.00 1
## 65 28 1 700 95 95 98 3.80 4.00 1
## 66 30 1 600 77 81 84 3.50 3.25 1
## 67 30 2 670 87 95 95 3.30 3.25 1
## 68 40 1 630 71 95 91 4.00 0.00 1
## 69 25 1 700 98 93 98 3.60 3.75 1
## 70 22 1 600 95 54 83 3.00 3.00 2
## 71 23 1 640 89 87 92 3.00 3.00 2
## 72 24 1 550 73 63 69 3.10 3.00 2
## 73 24 1 570 82 58 75 3.09 3.50 2
## 74 24 1 620 82 84 87 3.10 3.50 2
## 75 25 2 570 61 81 76 3.00 3.25 2
## 76 25 1 660 94 84 94 3.27 3.75 2
## 77 25 1 680 94 92 97 3.17 3.50 2
## 88 26 2 560 64 71 72 3.20 3.25 2
## 89 26 1 560 87 41 72 3.00 3.00 2
## 90 26 1 530 68 54 62 3.09 3.17 2
## 92 27 1 720 99 95 99 3.10 3.25 2
## 93 27 1 590 60 87 81 3.00 2.75 2
## 97 28 1 620 81 90 89 3.20 3.00 2
## 98 28 2 610 85 78 86 3.10 3.00 2
## 100 29 1 660 94 87 94 3.00 3.00 2
## 102 29 1 510 57 50 55 3.27 3.40 2
## 103 29 2 640 90 84 92 3.20 3.00 2
## 104 29 1 610 91 62 86 3.10 3.67 2
## 106 29 1 580 79 67 78 3.00 3.25 2
## 107 30 1 680 97 87 96 3.00 3.00 2
## 109 32 2 610 64 89 86 3.25 0.00 2
## 110 35 1 540 43 78 65 3.20 3.25 2
## 111 35 1 630 66 95 90 3.08 3.25 2
## 112 36 2 530 48 71 62 3.00 2.50 2
## 113 36 1 650 87 89 93 3.00 3.20 2
## 114 43 1 630 82 87 89 3.10 3.00 2
## 115 26 2 670 87 95 95 3.10 3.33 2
## 116 25 2 620 89 74 87 3.10 3.50 2
## 117 31 1 540 60 62 65 3.10 3.00 2
## 118 25 1 670 95 89 95 3.20 3.50 2
## 119 25 1 610 87 71 86 3.27 3.25 2
## 120 24 1 560 52 81 72 3.20 3.25 2
## 121 24 1 500 78 30 52 3.00 2.75 2
## 122 23 1 590 72 81 81 3.20 3.25 2
## 123 24 1 570 82 58 75 3.20 3.25 2
## 124 26 2 570 93 37 75 3.00 2.75 2
## 125 28 2 580 83 58 79 3.10 3.00 2
## 126 24 2 580 72 71 78 3.00 3.25 2
## 127 31 1 560 68 67 72 3.09 3.00 2
## 128 25 2 620 89 74 87 3.10 3.50 2
## 129 27 1 620 97 63 88 3.20 3.00 2
## 130 28 1 560 75 58 72 3.20 3.25 2
## 131 26 1 680 84 96 96 3.20 3.25 2
## 132 27 1 620 81 87 89 3.00 3.00 2
## 133 34 1 550 72 58 69 3.00 3.00 2
## 134 26 1 600 84 67 83 3.09 3.50 2
## 135 29 1 670 91 93 95 3.10 3.00 2
## 136 24 1 620 84 81 87 3.00 3.25 2
## 137 27 1 630 72 95 89 3.20 3.00 2
## 138 26 1 650 89 87 93 3.20 3.25 2
## 139 24 1 620 88 74 87 3.10 3.00 2
## 140 23 1 720 95 98 99 2.80 2.50 3
## 141 24 2 640 94 78 92 2.90 3.25 3
## 142 24 1 710 96 97 99 2.80 2.75 3
## 143 24 1 670 94 89 96 2.70 3.00 3
## 144 24 2 710 97 97 99 2.80 3.00 3
## 146 24 1 600 89 62 83 2.90 3.00 3
## 147 24 2 640 96 71 91 2.70 2.50 3
## 150 25 1 550 72 58 69 2.90 3.00 3
## 151 25 1 710 99 91 98 2.90 3.25 3
## 159 26 1 560 56 81 72 2.80 3.25 3
## 160 26 1 540 52 71 65 2.70 2.75 3
## 162 26 2 570 48 89 75 2.82 2.50 3
## 163 26 1 610 82 81 86 2.90 2.75 3
## 164 27 1 650 89 84 93 2.90 3.00 3
## 165 27 2 550 66 63 69 2.90 3.00 3
## 167 27 1 610 97 45 86 2.70 2.50 3
## 168 27 2 630 82 89 89 2.70 3.25 3
## 169 27 2 560 61 74 73 2.80 3.25 3
## 180 29 1 590 92 58 81 2.80 2.75 3
## 182 32 1 550 52 78 71 2.70 2.75 3
## 183 34 1 610 79 81 86 2.80 3.00 3
## 184 34 1 610 82 78 86 2.70 3.00 3
## 185 43 1 480 49 41 45 2.90 3.25 3
## 186 23 2 520 43 67 58 2.90 2.75 3
## 187 27 1 620 87 74 87 2.70 2.75 3
## 188 25 1 580 78 67 80 2.90 3.25 3
## 189 25 1 630 75 93 89 2.70 2.50 3
## 190 25 1 610 89 74 87 2.70 2.75 3
## 191 29 2 560 64 71 72 2.90 3.00 3
## 192 27 1 620 79 87 88 2.90 2.75 3
## 193 28 1 580 72 71 78 2.80 3.00 3
## 194 24 2 670 83 98 96 2.90 3.25 3
## 195 25 2 560 39 91 72 2.90 3.00 3
## 196 25 2 580 72 71 78 2.80 3.25 3
## 197 27 1 680 97 90 97 2.90 2.75 3
## 198 28 1 610 89 67 86 2.70 3.00 3
## 199 29 1 710 93 98 99 2.90 3.25 3
## 200 24 1 710 99 92 99 2.90 3.00 3
## 201 25 2 630 84 87 89 2.80 2.75 3
## 202 24 2 600 89 67 85 2.80 3.00 3
## 203 29 1 660 91 90 95 2.80 3.00 3
## 204 30 1 670 83 97 96 2.80 2.75 3
## 205 24 1 580 89 54 78 2.91 2.83 3
## 206 29 1 680 79 99 96 2.90 3.00 3
## 207 32 1 660 83 95 94 2.90 3.50 3
## 208 28 1 570 56 84 75 2.90 3.00 3
## 209 24 1 680 96 87 97 2.80 2.75 3
## 213 25 1 730 98 96 99 2.40 2.75 4
## 218 25 1 700 99 87 98 2.00 2.00 4
## 219 26 1 660 93 87 95 2.60 2.00 4
## 220 26 1 450 28 46 34 2.10 2.00 4
## 222 26 1 600 75 78 83 2.20 2.25 4
## 227 27 2 560 59 74 73 2.40 2.50 4
## 229 27 1 630 93 78 91 2.10 2.50 4
## 230 27 1 580 84 58 78 2.70 2.75 4
## 232 27 1 670 89 91 95 3.60 3.25 4
## 233 27 1 580 74 70 78 3.40 3.25 4
## 234 28 1 560 74 67 73 3.60 3.60 4
## 236 28 1 710 94 98 99 3.40 3.75 4
## 237 28 1 570 69 71 0 2.30 2.50 4
## 238 29 1 530 35 81 62 3.30 2.75 4
## 241 29 1 670 91 91 95 3.30 3.25 4
## 242 29 1 630 99 50 89 2.90 3.25 4
## 243 29 2 680 89 96 96 2.80 3.00 4
## 244 30 1 650 88 92 93 3.45 3.83 4
## 250 31 1 570 75 62 75 2.80 3.00 4
## 253 32 1 510 79 22 54 2.30 2.25 4
## 254 35 1 570 72 71 75 3.30 4.00 4
## 255 39 2 700 89 98 98 3.30 3.25 4
## 256 24 2 560 55 78 71 3.50 3.25 4
## 257 23 1 660 81 98 95 2.50 3.00 4
## 258 25 2 720 96 98 99 3.50 3.60 4
## 259 26 1 620 78 87 89 2.40 2.00 4
## 260 26 2 630 85 81 90 2.90 3.25 4
## 261 27 1 650 89 89 93 2.40 2.25 4
## 262 25 1 660 99 71 95 3.40 3.25 4
## 263 25 1 610 83 81 86 2.40 2.75 4
## 264 26 1 600 87 62 83 2.50 2.50 4
## 265 24 1 570 75 62 75 2.30 2.50 4
## 266 24 2 600 77 78 84 2.60 3.00 4
## 267 26 2 650 91 84 93 2.60 3.00 4
## 268 29 1 630 72 95 89 2.60 2.50 4
## 269 26 1 630 96 71 91 2.60 2.75 4
## 270 31 1 530 75 45 62 2.40 2.75 4
## 271 23 1 580 64 81 78 2.20 2.00 4
## 272 25 1 540 79 45 65 2.60 2.50 4
## 273 26 1 550 72 58 69 2.60 2.75 4
## 274 40 2 500 60 45 51 2.50 2.75 4
## work_yrs frstlang salary satis
## 1 2 1 0 7
## 2 2 1 0 6
## 3 2 1 0 6
## 4 1 1 0 7
## 6 2 1 0 6
## 7 2 1 0 5
## 8 2 1 0 6
## 22 3 1 0 6
## 23 1 2 0 5
## 24 5 1 0 5
## 25 3 1 0 5
## 27 10 1 0 7
## 28 5 1 0 5
## 29 7 1 0 6
## 31 7 1 0 6
## 32 9 1 0 6
## 33 13 1 0 5
## 34 22 1 0 6
## 35 1 1 85000 5
## 36 2 1 85000 6
## 37 2 1 86000 5
## 38 3 1 88000 7
## 39 2 1 92000 6
## 40 5 1 93000 5
## 41 0 1 95000 4
## 42 1 1 95000 5
## 43 3 1 95000 3
## 44 1 1 96000 7
## 45 4 1 96000 5
## 46 2 1 100000 7
## 47 2 1 100000 6
## 48 6 1 100000 6
## 49 2 1 105000 7
## 50 3 1 105000 6
## 51 3 1 105000 6
## 52 2 1 105000 5
## 53 5 1 105000 6
## 54 6 1 105000 6
## 55 8 1 106000 7
## 56 6 1 106000 6
## 57 3 1 107500 5
## 58 3 1 108000 6
## 59 6 1 110000 5
## 60 16 1 112000 7
## 61 4 1 115000 5
## 62 1 1 115000 5
## 63 10 2 118000 7
## 64 3 1 120000 5
## 65 5 1 120000 5
## 66 5 1 120000 6
## 67 8 1 120000 6
## 68 15 1 146000 6
## 69 1 1 162000 5
## 70 1 1 0 5
## 71 2 1 0 7
## 72 0 2 0 5
## 73 2 1 0 6
## 74 1 1 0 5
## 75 3 1 0 4
## 76 2 1 0 5
## 77 2 1 0 6
## 88 3 1 0 6
## 89 3 1 0 6
## 90 4 2 0 5
## 92 5 1 0 5
## 93 3 1 0 6
## 97 4 1 0 6
## 98 5 1 0 6
## 100 1 1 0 6
## 102 5 1 0 5
## 103 3 1 0 5
## 104 7 1 0 5
## 106 4 1 0 6
## 107 4 1 0 5
## 109 11 1 0 7
## 110 8 1 0 5
## 111 12 1 0 5
## 112 7 1 0 5
## 113 18 1 0 6
## 114 16 1 0 5
## 115 1 1 82000 7
## 116 2 1 92000 5
## 117 8 1 93000 6
## 118 2 1 95000 6
## 119 3 1 95000 6
## 120 2 1 96000 7
## 121 2 1 96500 6
## 122 2 1 98000 6
## 123 2 1 98000 6
## 124 3 2 98000 5
## 125 5 2 99000 6
## 126 2 1 100000 5
## 127 4 1 100000 6
## 128 2 1 101000 5
## 129 3 1 103000 6
## 130 4 1 104000 5
## 131 3 1 105000 6
## 132 3 1 105000 5
## 133 16 1 105000 5
## 134 2 1 107000 5
## 135 6 1 112000 6
## 136 1 1 115000 6
## 137 4 1 115000 6
## 138 4 1 130000 7
## 139 2 1 145800 6
## 140 1 1 0 5
## 141 2 2 0 4
## 142 2 1 0 7
## 143 2 1 0 7
## 144 2 1 0 7
## 146 1 1 0 6
## 147 2 1 0 6
## 150 3 1 0 6
## 151 1 1 0 6
## 159 4 1 0 6
## 160 2 1 0 6
## 162 3 1 0 5
## 163 3 1 0 6
## 164 2 1 0 6
## 165 3 1 0 4
## 167 4 2 0 5
## 168 5 1 0 6
## 169 5 1 0 6
## 180 3 2 0 5
## 182 7 1 0 6
## 183 11 1 0 6
## 184 12 1 0 5
## 185 22 1 0 5
## 186 1 1 78256 5
## 187 3 1 88500 6
## 188 2 1 90000 7
## 189 2 1 90000 5
## 190 4 1 93000 6
## 191 5 1 95000 7
## 192 4 1 97000 7
## 193 3 1 97000 6
## 194 2 1 98000 7
## 195 2 1 98000 7
## 196 2 1 98000 6
## 197 2 2 98000 6
## 198 4 1 98000 7
## 199 7 1 98000 5
## 200 3 1 100000 6
## 201 2 1 100000 6
## 202 2 1 101000 6
## 203 8 1 101100 6
## 204 6 1 102500 5
## 205 2 1 105000 5
## 206 6 1 106000 6
## 207 2 2 107300 7
## 208 4 1 108000 6
## 209 2 1 112000 6
## 213 2 1 0 6
## 218 1 1 0 7
## 219 2 1 0 5
## 220 4 1 0 6
## 222 2 1 0 6
## 227 2 1 0 5
## 229 4 1 0 5
## 230 1 1 0 5
## 232 5 1 0 6
## 233 3 1 0 6
## 234 5 1 0 5
## 236 6 1 0 6
## 237 5 1 0 5
## 238 6 1 0 7
## 241 3 1 0 5
## 242 1 2 0 4
## 243 4 1 0 5
## 244 2 1 0 6
## 250 1 1 0 6
## 253 5 2 0 5
## 254 8 1 0 6
## 255 5 1 0 5
## 256 2 1 64000 7
## 257 2 1 77000 6
## 258 3 1 85000 6
## 259 2 1 85000 6
## 260 3 1 86000 5
## 261 5 1 90000 5
## 262 2 1 92000 7
## 263 2 1 95000 7
## 264 2 1 96000 6
## 265 2 1 98000 6
## 266 2 1 100000 6
## 267 2 1 100000 7
## 268 3 1 100400 7
## 269 3 1 101600 6
## 270 4 2 104000 6
## 271 2 1 105000 6
## 272 3 1 115000 5
## 273 3 1 126710 6
## 274 15 2 220000 6str(mba2.df)## 'data.frame': 193 obs. of 13 variables:
## $ age : int 23 24 24 24 24 25 25 27 27 28 ...
## $ sex : int 2 1 1 1 1 1 2 1 1 2 ...
## $ gmat_tot: int 620 610 670 570 640 610 650 740 750 540 ...
## $ gmat_qpc: int 77 90 99 56 82 89 88 99 99 75 ...
## $ gmat_vpc: int 87 71 78 81 89 74 89 96 98 50 ...
## $ gmat_tpc: int 87 87 95 75 91 87 92 99 99 65 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.9 3.4 3.3 3.5 3.4 3.6 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.5 3.75 3.5 3.5 4 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 3 1 5 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 0 0 0 0 0 0 ...
## $ satis : int 7 6 6 7 6 5 6 6 5 5 ...- placed and disclosed their salary and satisfaction
mba3.df <- mba.df[which(mba.df$salary!= 998 & mba.df$salary!= 999 & mba.df$satis!= 998 & mba.df$salary!= 0), ]
mba3.df## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 35 22 2 660 90 92 94 3.50 3.75 1
## 36 27 2 700 94 98 98 3.30 3.25 1
## 37 25 2 680 87 96 96 3.50 2.67 1
## 38 25 2 650 82 91 93 3.40 3.25 1
## 39 27 1 710 96 96 98 3.30 3.50 1
## 40 28 2 620 52 98 87 3.40 3.75 1
## 41 24 1 670 84 96 95 3.30 3.25 1
## 42 25 2 560 52 81 72 3.30 3.50 1
## 43 25 2 530 50 62 61 3.60 3.67 1
## 44 25 1 650 79 93 93 3.30 3.50 1
## 45 26 2 590 56 89 81 3.30 3.25 1
## 46 23 2 650 93 81 93 3.40 3.00 1
## 47 24 1 560 81 50 71 3.40 3.67 1
## 48 27 1 610 72 84 86 3.30 3.50 1
## 49 25 1 650 95 84 93 3.30 3.00 1
## 50 25 1 550 74 50 68 3.50 3.50 1
## 51 26 1 570 68 74 75 3.80 3.50 1
## 52 26 1 580 79 71 78 3.45 3.50 1
## 53 30 1 600 60 91 83 3.30 3.25 1
## 54 31 1 570 72 71 75 3.60 3.50 1
## 55 30 1 620 60 96 87 3.50 3.00 1
## 56 30 2 680 96 87 96 3.70 3.60 1
## 57 27 1 630 93 75 91 3.30 3.25 1
## 58 25 1 600 82 74 83 3.50 3.25 1
## 59 28 2 640 89 81 91 3.60 3.50 1
## 60 39 1 600 72 81 83 3.60 3.50 1
## 61 27 1 570 95 33 75 3.70 4.00 1
## 62 27 1 710 95 98 98 3.60 3.50 1
## 63 33 1 620 72 89 87 3.50 3.50 1
## 64 27 1 600 67 84 83 3.50 3.00 1
## 65 28 1 700 95 95 98 3.80 4.00 1
## 66 30 1 600 77 81 84 3.50 3.25 1
## 67 30 2 670 87 95 95 3.30 3.25 1
## 68 40 1 630 71 95 91 4.00 0.00 1
## 69 25 1 700 98 93 98 3.60 3.75 1
## 115 26 2 670 87 95 95 3.10 3.33 2
## 116 25 2 620 89 74 87 3.10 3.50 2
## 117 31 1 540 60 62 65 3.10 3.00 2
## 118 25 1 670 95 89 95 3.20 3.50 2
## 119 25 1 610 87 71 86 3.27 3.25 2
## 120 24 1 560 52 81 72 3.20 3.25 2
## 121 24 1 500 78 30 52 3.00 2.75 2
## 122 23 1 590 72 81 81 3.20 3.25 2
## 123 24 1 570 82 58 75 3.20 3.25 2
## 124 26 2 570 93 37 75 3.00 2.75 2
## 125 28 2 580 83 58 79 3.10 3.00 2
## 126 24 2 580 72 71 78 3.00 3.25 2
## 127 31 1 560 68 67 72 3.09 3.00 2
## 128 25 2 620 89 74 87 3.10 3.50 2
## 129 27 1 620 97 63 88 3.20 3.00 2
## 130 28 1 560 75 58 72 3.20 3.25 2
## 131 26 1 680 84 96 96 3.20 3.25 2
## 132 27 1 620 81 87 89 3.00 3.00 2
## 133 34 1 550 72 58 69 3.00 3.00 2
## 134 26 1 600 84 67 83 3.09 3.50 2
## 135 29 1 670 91 93 95 3.10 3.00 2
## 136 24 1 620 84 81 87 3.00 3.25 2
## 137 27 1 630 72 95 89 3.20 3.00 2
## 138 26 1 650 89 87 93 3.20 3.25 2
## 139 24 1 620 88 74 87 3.10 3.00 2
## 186 23 2 520 43 67 58 2.90 2.75 3
## 187 27 1 620 87 74 87 2.70 2.75 3
## 188 25 1 580 78 67 80 2.90 3.25 3
## 189 25 1 630 75 93 89 2.70 2.50 3
## 190 25 1 610 89 74 87 2.70 2.75 3
## 191 29 2 560 64 71 72 2.90 3.00 3
## 192 27 1 620 79 87 88 2.90 2.75 3
## 193 28 1 580 72 71 78 2.80 3.00 3
## 194 24 2 670 83 98 96 2.90 3.25 3
## 195 25 2 560 39 91 72 2.90 3.00 3
## 196 25 2 580 72 71 78 2.80 3.25 3
## 197 27 1 680 97 90 97 2.90 2.75 3
## 198 28 1 610 89 67 86 2.70 3.00 3
## 199 29 1 710 93 98 99 2.90 3.25 3
## 200 24 1 710 99 92 99 2.90 3.00 3
## 201 25 2 630 84 87 89 2.80 2.75 3
## 202 24 2 600 89 67 85 2.80 3.00 3
## 203 29 1 660 91 90 95 2.80 3.00 3
## 204 30 1 670 83 97 96 2.80 2.75 3
## 205 24 1 580 89 54 78 2.91 2.83 3
## 206 29 1 680 79 99 96 2.90 3.00 3
## 207 32 1 660 83 95 94 2.90 3.50 3
## 208 28 1 570 56 84 75 2.90 3.00 3
## 209 24 1 680 96 87 97 2.80 2.75 3
## 256 24 2 560 55 78 71 3.50 3.25 4
## 257 23 1 660 81 98 95 2.50 3.00 4
## 258 25 2 720 96 98 99 3.50 3.60 4
## 259 26 1 620 78 87 89 2.40 2.00 4
## 260 26 2 630 85 81 90 2.90 3.25 4
## 261 27 1 650 89 89 93 2.40 2.25 4
## 262 25 1 660 99 71 95 3.40 3.25 4
## 263 25 1 610 83 81 86 2.40 2.75 4
## 264 26 1 600 87 62 83 2.50 2.50 4
## 265 24 1 570 75 62 75 2.30 2.50 4
## 266 24 2 600 77 78 84 2.60 3.00 4
## 267 26 2 650 91 84 93 2.60 3.00 4
## 268 29 1 630 72 95 89 2.60 2.50 4
## 269 26 1 630 96 71 91 2.60 2.75 4
## 270 31 1 530 75 45 62 2.40 2.75 4
## 271 23 1 580 64 81 78 2.20 2.00 4
## 272 25 1 540 79 45 65 2.60 2.50 4
## 273 26 1 550 72 58 69 2.60 2.75 4
## 274 40 2 500 60 45 51 2.50 2.75 4
## work_yrs frstlang salary satis
## 35 1 1 85000 5
## 36 2 1 85000 6
## 37 2 1 86000 5
## 38 3 1 88000 7
## 39 2 1 92000 6
## 40 5 1 93000 5
## 41 0 1 95000 4
## 42 1 1 95000 5
## 43 3 1 95000 3
## 44 1 1 96000 7
## 45 4 1 96000 5
## 46 2 1 100000 7
## 47 2 1 100000 6
## 48 6 1 100000 6
## 49 2 1 105000 7
## 50 3 1 105000 6
## 51 3 1 105000 6
## 52 2 1 105000 5
## 53 5 1 105000 6
## 54 6 1 105000 6
## 55 8 1 106000 7
## 56 6 1 106000 6
## 57 3 1 107500 5
## 58 3 1 108000 6
## 59 6 1 110000 5
## 60 16 1 112000 7
## 61 4 1 115000 5
## 62 1 1 115000 5
## 63 10 2 118000 7
## 64 3 1 120000 5
## 65 5 1 120000 5
## 66 5 1 120000 6
## 67 8 1 120000 6
## 68 15 1 146000 6
## 69 1 1 162000 5
## 115 1 1 82000 7
## 116 2 1 92000 5
## 117 8 1 93000 6
## 118 2 1 95000 6
## 119 3 1 95000 6
## 120 2 1 96000 7
## 121 2 1 96500 6
## 122 2 1 98000 6
## 123 2 1 98000 6
## 124 3 2 98000 5
## 125 5 2 99000 6
## 126 2 1 100000 5
## 127 4 1 100000 6
## 128 2 1 101000 5
## 129 3 1 103000 6
## 130 4 1 104000 5
## 131 3 1 105000 6
## 132 3 1 105000 5
## 133 16 1 105000 5
## 134 2 1 107000 5
## 135 6 1 112000 6
## 136 1 1 115000 6
## 137 4 1 115000 6
## 138 4 1 130000 7
## 139 2 1 145800 6
## 186 1 1 78256 5
## 187 3 1 88500 6
## 188 2 1 90000 7
## 189 2 1 90000 5
## 190 4 1 93000 6
## 191 5 1 95000 7
## 192 4 1 97000 7
## 193 3 1 97000 6
## 194 2 1 98000 7
## 195 2 1 98000 7
## 196 2 1 98000 6
## 197 2 2 98000 6
## 198 4 1 98000 7
## 199 7 1 98000 5
## 200 3 1 100000 6
## 201 2 1 100000 6
## 202 2 1 101000 6
## 203 8 1 101100 6
## 204 6 1 102500 5
## 205 2 1 105000 5
## 206 6 1 106000 6
## 207 2 2 107300 7
## 208 4 1 108000 6
## 209 2 1 112000 6
## 256 2 1 64000 7
## 257 2 1 77000 6
## 258 3 1 85000 6
## 259 2 1 85000 6
## 260 3 1 86000 5
## 261 5 1 90000 5
## 262 2 1 92000 7
## 263 2 1 95000 7
## 264 2 1 96000 6
## 265 2 1 98000 6
## 266 2 1 100000 6
## 267 2 1 100000 7
## 268 3 1 100400 7
## 269 3 1 101600 6
## 270 4 2 104000 6
## 271 2 1 105000 6
## 272 3 1 115000 5
## 273 3 1 126710 6
## 274 15 2 220000 6str(mba3.df)## 'data.frame': 103 obs. of 13 variables:
## $ age : int 22 27 25 25 27 28 24 25 25 25 ...
## $ sex : int 2 2 2 2 1 2 1 2 2 1 ...
## $ gmat_tot: int 660 700 680 650 710 620 670 560 530 650 ...
## $ gmat_qpc: int 90 94 87 82 96 52 84 52 50 79 ...
## $ gmat_vpc: int 92 98 96 91 96 98 96 81 62 93 ...
## $ gmat_tpc: int 94 98 96 93 98 87 95 72 61 93 ...
## $ s_avg : num 3.5 3.3 3.5 3.4 3.3 3.4 3.3 3.3 3.6 3.3 ...
## $ f_avg : num 3.75 3.25 2.67 3.25 3.5 3.75 3.25 3.5 3.67 3.5 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 1 2 2 3 2 5 0 1 3 1 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 1 1 ...
## $ salary : int 85000 85000 86000 88000 92000 93000 95000 95000 95000 96000 ...
## $ satis : int 5 6 5 7 6 5 4 5 3 7 ...- disclosed and not placed
mba4.df <- mba.df[which(mba.df$salary!=998 & mba.df$salary!=999 & mba.df$salary==0 & mba.df$satis!= 998), ]
mba4.df## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 1 23 2 620 77 87 87 3.40 3.00 1
## 2 24 1 610 90 71 87 3.50 4.00 1
## 3 24 1 670 99 78 95 3.30 3.25 1
## 4 24 1 570 56 81 75 3.30 2.67 1
## 6 24 1 640 82 89 91 3.90 3.75 1
## 7 25 1 610 89 74 87 3.40 3.50 1
## 8 25 2 650 88 89 92 3.30 3.75 1
## 22 27 1 740 99 96 99 3.50 3.50 1
## 23 27 1 750 99 98 99 3.40 3.50 1
## 24 28 2 540 75 50 65 3.60 4.00 1
## 25 29 1 580 56 87 78 3.64 3.33 1
## 27 31 2 560 60 78 72 3.30 3.75 1
## 28 32 1 760 99 99 99 3.40 3.00 1
## 29 32 1 640 79 91 91 3.60 3.75 1
## 31 34 2 620 75 89 87 3.30 3.00 1
## 32 37 2 560 43 87 72 3.40 3.50 1
## 33 42 2 650 75 98 93 3.38 3.00 1
## 34 48 1 590 84 62 81 3.80 4.00 1
## 70 22 1 600 95 54 83 3.00 3.00 2
## 71 23 1 640 89 87 92 3.00 3.00 2
## 72 24 1 550 73 63 69 3.10 3.00 2
## 73 24 1 570 82 58 75 3.09 3.50 2
## 74 24 1 620 82 84 87 3.10 3.50 2
## 75 25 2 570 61 81 76 3.00 3.25 2
## 76 25 1 660 94 84 94 3.27 3.75 2
## 77 25 1 680 94 92 97 3.17 3.50 2
## 88 26 2 560 64 71 72 3.20 3.25 2
## 89 26 1 560 87 41 72 3.00 3.00 2
## 90 26 1 530 68 54 62 3.09 3.17 2
## 92 27 1 720 99 95 99 3.10 3.25 2
## 93 27 1 590 60 87 81 3.00 2.75 2
## 97 28 1 620 81 90 89 3.20 3.00 2
## 98 28 2 610 85 78 86 3.10 3.00 2
## 100 29 1 660 94 87 94 3.00 3.00 2
## 102 29 1 510 57 50 55 3.27 3.40 2
## 103 29 2 640 90 84 92 3.20 3.00 2
## 104 29 1 610 91 62 86 3.10 3.67 2
## 106 29 1 580 79 67 78 3.00 3.25 2
## 107 30 1 680 97 87 96 3.00 3.00 2
## 109 32 2 610 64 89 86 3.25 0.00 2
## 110 35 1 540 43 78 65 3.20 3.25 2
## 111 35 1 630 66 95 90 3.08 3.25 2
## 112 36 2 530 48 71 62 3.00 2.50 2
## 113 36 1 650 87 89 93 3.00 3.20 2
## 114 43 1 630 82 87 89 3.10 3.00 2
## 140 23 1 720 95 98 99 2.80 2.50 3
## 141 24 2 640 94 78 92 2.90 3.25 3
## 142 24 1 710 96 97 99 2.80 2.75 3
## 143 24 1 670 94 89 96 2.70 3.00 3
## 144 24 2 710 97 97 99 2.80 3.00 3
## 146 24 1 600 89 62 83 2.90 3.00 3
## 147 24 2 640 96 71 91 2.70 2.50 3
## 150 25 1 550 72 58 69 2.90 3.00 3
## 151 25 1 710 99 91 98 2.90 3.25 3
## 159 26 1 560 56 81 72 2.80 3.25 3
## 160 26 1 540 52 71 65 2.70 2.75 3
## 162 26 2 570 48 89 75 2.82 2.50 3
## 163 26 1 610 82 81 86 2.90 2.75 3
## 164 27 1 650 89 84 93 2.90 3.00 3
## 165 27 2 550 66 63 69 2.90 3.00 3
## 167 27 1 610 97 45 86 2.70 2.50 3
## 168 27 2 630 82 89 89 2.70 3.25 3
## 169 27 2 560 61 74 73 2.80 3.25 3
## 180 29 1 590 92 58 81 2.80 2.75 3
## 182 32 1 550 52 78 71 2.70 2.75 3
## 183 34 1 610 79 81 86 2.80 3.00 3
## 184 34 1 610 82 78 86 2.70 3.00 3
## 185 43 1 480 49 41 45 2.90 3.25 3
## 213 25 1 730 98 96 99 2.40 2.75 4
## 218 25 1 700 99 87 98 2.00 2.00 4
## 219 26 1 660 93 87 95 2.60 2.00 4
## 220 26 1 450 28 46 34 2.10 2.00 4
## 222 26 1 600 75 78 83 2.20 2.25 4
## 227 27 2 560 59 74 73 2.40 2.50 4
## 229 27 1 630 93 78 91 2.10 2.50 4
## 230 27 1 580 84 58 78 2.70 2.75 4
## 232 27 1 670 89 91 95 3.60 3.25 4
## 233 27 1 580 74 70 78 3.40 3.25 4
## 234 28 1 560 74 67 73 3.60 3.60 4
## 236 28 1 710 94 98 99 3.40 3.75 4
## 237 28 1 570 69 71 0 2.30 2.50 4
## 238 29 1 530 35 81 62 3.30 2.75 4
## 241 29 1 670 91 91 95 3.30 3.25 4
## 242 29 1 630 99 50 89 2.90 3.25 4
## 243 29 2 680 89 96 96 2.80 3.00 4
## 244 30 1 650 88 92 93 3.45 3.83 4
## 250 31 1 570 75 62 75 2.80 3.00 4
## 253 32 1 510 79 22 54 2.30 2.25 4
## 254 35 1 570 72 71 75 3.30 4.00 4
## 255 39 2 700 89 98 98 3.30 3.25 4
## work_yrs frstlang salary satis
## 1 2 1 0 7
## 2 2 1 0 6
## 3 2 1 0 6
## 4 1 1 0 7
## 6 2 1 0 6
## 7 2 1 0 5
## 8 2 1 0 6
## 22 3 1 0 6
## 23 1 2 0 5
## 24 5 1 0 5
## 25 3 1 0 5
## 27 10 1 0 7
## 28 5 1 0 5
## 29 7 1 0 6
## 31 7 1 0 6
## 32 9 1 0 6
## 33 13 1 0 5
## 34 22 1 0 6
## 70 1 1 0 5
## 71 2 1 0 7
## 72 0 2 0 5
## 73 2 1 0 6
## 74 1 1 0 5
## 75 3 1 0 4
## 76 2 1 0 5
## 77 2 1 0 6
## 88 3 1 0 6
## 89 3 1 0 6
## 90 4 2 0 5
## 92 5 1 0 5
## 93 3 1 0 6
## 97 4 1 0 6
## 98 5 1 0 6
## 100 1 1 0 6
## 102 5 1 0 5
## 103 3 1 0 5
## 104 7 1 0 5
## 106 4 1 0 6
## 107 4 1 0 5
## 109 11 1 0 7
## 110 8 1 0 5
## 111 12 1 0 5
## 112 7 1 0 5
## 113 18 1 0 6
## 114 16 1 0 5
## 140 1 1 0 5
## 141 2 2 0 4
## 142 2 1 0 7
## 143 2 1 0 7
## 144 2 1 0 7
## 146 1 1 0 6
## 147 2 1 0 6
## 150 3 1 0 6
## 151 1 1 0 6
## 159 4 1 0 6
## 160 2 1 0 6
## 162 3 1 0 5
## 163 3 1 0 6
## 164 2 1 0 6
## 165 3 1 0 4
## 167 4 2 0 5
## 168 5 1 0 6
## 169 5 1 0 6
## 180 3 2 0 5
## 182 7 1 0 6
## 183 11 1 0 6
## 184 12 1 0 5
## 185 22 1 0 5
## 213 2 1 0 6
## 218 1 1 0 7
## 219 2 1 0 5
## 220 4 1 0 6
## 222 2 1 0 6
## 227 2 1 0 5
## 229 4 1 0 5
## 230 1 1 0 5
## 232 5 1 0 6
## 233 3 1 0 6
## 234 5 1 0 5
## 236 6 1 0 6
## 237 5 1 0 5
## 238 6 1 0 7
## 241 3 1 0 5
## 242 1 2 0 4
## 243 4 1 0 5
## 244 2 1 0 6
## 250 1 1 0 6
## 253 5 2 0 5
## 254 8 1 0 6
## 255 5 1 0 5str(mba4.df)## 'data.frame': 90 obs. of 13 variables:
## $ age : int 23 24 24 24 24 25 25 27 27 28 ...
## $ sex : int 2 1 1 1 1 1 2 1 1 2 ...
## $ gmat_tot: int 620 610 670 570 640 610 650 740 750 540 ...
## $ gmat_qpc: int 77 90 99 56 82 89 88 99 99 75 ...
## $ gmat_vpc: int 87 71 78 81 89 74 89 96 98 50 ...
## $ gmat_tpc: int 87 87 95 75 91 87 92 99 99 65 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.9 3.4 3.3 3.5 3.4 3.6 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.5 3.75 3.5 3.5 4 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 3 1 5 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 0 0 0 0 0 0 ...
## $ satis : int 7 6 6 7 6 5 6 6 5 5 ...library(psych)## Warning: package 'psych' was built under R version 3.3.3describe(mba1.df)## vars n mean sd median trimmed mad min max
## age 1 193 27.59 4.22 27.00 26.86 2.97 22 48
## sex 2 193 1.28 0.45 1.00 1.23 0.00 1 2
## gmat_tot 3 193 615.23 56.54 610.00 614.19 59.30 450 760
## gmat_qpc 4 193 79.35 15.15 82.00 80.92 14.83 28 99
## gmat_vpc 5 193 78.13 16.10 81.00 79.87 14.83 22 99
## gmat_tpc 6 193 83.48 13.53 87.00 85.08 11.86 0 99
## s_avg 7 193 3.06 0.38 3.09 3.08 0.43 2 4
## f_avg 8 193 3.08 0.52 3.00 3.11 0.37 0 4
## quarter 9 193 2.39 1.10 2.00 2.37 1.48 1 4
## work_yrs 10 193 4.10 3.69 3.00 3.37 1.48 0 22
## frstlang 11 193 1.08 0.27 1.00 1.00 0.00 1 2
## salary 12 193 54985.32 53152.39 85000.00 52726.81 51891.00 0 220000
## satis 13 193 5.76 0.77 6.00 5.75 1.48 3 7
## range skew kurtosis se
## age 26 1.93 4.55 0.30
## sex 1 0.97 -1.06 0.03
## gmat_tot 310 0.08 -0.31 4.07
## gmat_qpc 71 -0.88 0.23 1.09
## gmat_vpc 77 -0.90 0.36 1.16
## gmat_tpc 99 -1.87 7.03 0.97
## s_avg 2 -0.27 -0.15 0.03
## f_avg 4 -2.17 11.03 0.04
## quarter 3 0.13 -1.32 0.08
## work_yrs 22 2.47 7.02 0.27
## frstlang 1 3.13 7.84 0.02
## salary 220000 0.10 -1.45 3825.99
## satis 4 -0.17 -0.06 0.06mean of all variables
1.age 2.gmat total 3.gmat quants 4.gmat verbal 5.gmat percentile 6.summer average 7.fall average
mean(mba.df$age)## [1] 27.35766mean(mba.df$gmat_tot)## [1] 619.4526mean(mba.df$gmat_qpc)## [1] 80.64234mean(mba.df$gmat_vpc)## [1] 78.32117mean(mba.df$gmat_tpc)## [1] 84.19708mean(mba.df$s_avg)## [1] 3.025401mean(mba.df$f_avg)## [1] 3.061533Mean, median and sd of salary for those information available
mean(mba1.df$salary)## [1] 54985.32median(mba1.df$salary)## [1] 85000sd(mba1.df$salary)## [1] 53152.39max(mba1.df$salary)## [1] 220000min(mba1.df$salary)## [1] 0Satisfaction
mean(mba1.df$satis)## [1] 5.761658max(mba1.df$satis)## [1] 7min(mba1.df$satis)## [1] 3BOX Plot for various variable
boxplot(mba.df$age)
hist(mba.df$sex, main = "Sex")
par(mfrow = c(2,2))
boxplot(mba.df$gmat_tot, main="Total GMAT score")
boxplot(mba.df$gmat_qpc, main="Quantitative GMAT percentile")
boxplot(mba.df$gmat_vpc, main="Verbal GMAT percentile")
boxplot(mba.df$gmat_tpc, main="Overall GMAT percentile")
par(mfrow = c(1,2))
boxplot(mba.df$s_avg, main="Spring MBA average")
boxplot(mba.df$f_avg, main="Fall MBA average")
par(mfrow = c(1,2))
boxplot(mba1.df$salary, main = "Salary")
barplot(mba1.df$salary)
par(mfrow = c(1,2))
boxplot(mba1.df$satis, main="Degree of satisfaction")
barplot(mba1.df$satis, main="Degree of satisfaction")
Scatterplots between the variables
plot(mba1.df$salary, mba1.df$age, main="Salary vs Age")
plot(mba1.df$salary, mba1.df$gmat_tot, main="Salary vs Total GMAT score")
plot(mba1.df$salary, mba1.df$gmat_qpc, main="Salary vs Quantitative GMAT percentile")
plot(mba1.df$salary, mba1.df$gmat_vpc, main="Salary vs Verbal GMAT percentile")
plot(mba1.df$salary, mba1.df$gmat_tpc, main="Salary vs Overall GMAT percentile")
plot(mba1.df$salary, mba1.df$s_avg, main="Salary vs Spring MBA average")
plot(mba1.df$salary, mba1.df$f_avg, main="Salary vs Fall MBA average")
plot(mba1.df$salary, mba1.df$quarter, main="Salary vs Quartile ranking")
plot(mba1.df$salary, mba1.df$satis, main="Salary vs Degree of satisfaction")
Correlation plot of the variables
library(corrplot) ## Warning: package 'corrplot' was built under R version 3.3.3## corrplot 0.84 loadedcorrplot(corr=cor(mba1.df[ , c(1:13)], use="complete.obs"),
method ="ellipse")
Corrgram of all the variables
library(corrgram)## Warning: package 'corrgram' was built under R version 3.3.3corrgram(mba1.df, order=TRUE,
main="Corrgram of all the Variables",
lower.panel=panel.shade, upper.panel=panel.pie,
diag.panel=panel.minmax, text.panel=panel.txt) 
## Corelation test
x <- mba1.df[,c("age", "gmat_tot","gmat_qpc","gmat_vpc","gmat_tpc","s_avg", "f_avg","work_yrs")]
y <- mba1.df[,c("salary", "satis")]
cor(x,y)## salary satis
## age -1.301987e-01 -0.073500580
## gmat_tot -5.685962e-05 0.079819458
## gmat_qpc 2.839164e-02 -0.020006117
## gmat_vpc 3.389965e-03 0.195134711
## gmat_tpc 6.094464e-02 0.132884339
## s_avg 9.632412e-02 -0.046399534
## f_avg 8.846655e-03 -0.114704819
## work_yrs -5.326685e-02 -0.007722658Salary and gender
mytable <- xtabs(~ salary+sex,data= mba3.df)
mytable## sex
## salary 1 2
## 64000 0 1
## 77000 1 0
## 78256 0 1
## 82000 0 1
## 85000 1 3
## 86000 0 2
## 88000 0 1
## 88500 1 0
## 90000 3 0
## 92000 2 1
## 93000 2 1
## 95000 4 3
## 96000 3 1
## 96500 1 0
## 97000 2 0
## 98000 6 4
## 99000 0 1
## 100000 4 5
## 100400 1 0
## 101000 0 2
## 101100 1 0
## 101600 1 0
## 102500 1 0
## 103000 1 0
## 104000 2 0
## 105000 11 0
## 106000 2 1
## 107000 1 0
## 107300 1 0
## 107500 1 0
## 108000 2 0
## 110000 0 1
## 112000 3 0
## 115000 5 0
## 118000 1 0
## 120000 3 1
## 126710 1 0
## 130000 1 0
## 145800 1 0
## 146000 1 0
## 162000 1 0
## 220000 0 1malesalary <- xtabs(~ salary+sex, data = mba3.df, sex==1)
femalesalary <- xtabs(~ salary+sex, data = mba3.df, sex==2)margin.table(mytable,2)## sex
## 1 2
## 72 31aggregate(mba3.df$salary, by=list(sex = mba3.df$sex), mean)## sex x
## 1 1 104970.97
## 2 2 98524.39chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 52.681, df = 41, p-value = 0.1045p > 0.01 suggest that their is no relationship between salary and gender.
salary and first language
mytable <- xtabs(~ salary+frstlang,data= mba3.df)
mytable## frstlang
## salary 1 2
## 64000 1 0
## 77000 1 0
## 78256 1 0
## 82000 1 0
## 85000 4 0
## 86000 2 0
## 88000 1 0
## 88500 1 0
## 90000 3 0
## 92000 3 0
## 93000 3 0
## 95000 7 0
## 96000 4 0
## 96500 1 0
## 97000 2 0
## 98000 8 2
## 99000 0 1
## 100000 9 0
## 100400 1 0
## 101000 2 0
## 101100 1 0
## 101600 1 0
## 102500 1 0
## 103000 1 0
## 104000 1 1
## 105000 11 0
## 106000 3 0
## 107000 1 0
## 107300 0 1
## 107500 1 0
## 108000 2 0
## 110000 1 0
## 112000 3 0
## 115000 5 0
## 118000 0 1
## 120000 4 0
## 126710 1 0
## 130000 1 0
## 145800 1 0
## 146000 1 0
## 162000 1 0
## 220000 0 1aggregate(mba3.df$salary, by=list(language = mba3.df$frstlang), mean)## language x
## 1 1 101748.6
## 2 2 120614.3margin.table(mytable,2)## frstlang
## 1 2
## 96 7chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 69.847, df = 41, p-value = 0.003296p < 0.05 suggest that is some relationship between salary and first language
salary and prior work experiance
mytable <- xtabs(~ salary+work_yrs,data= mba3.df)
mytable## work_yrs
## salary 0 1 2 3 4 5 6 7 8 10 15 16
## 64000 0 0 1 0 0 0 0 0 0 0 0 0
## 77000 0 0 1 0 0 0 0 0 0 0 0 0
## 78256 0 1 0 0 0 0 0 0 0 0 0 0
## 82000 0 1 0 0 0 0 0 0 0 0 0 0
## 85000 0 1 2 1 0 0 0 0 0 0 0 0
## 86000 0 0 1 1 0 0 0 0 0 0 0 0
## 88000 0 0 0 1 0 0 0 0 0 0 0 0
## 88500 0 0 0 1 0 0 0 0 0 0 0 0
## 90000 0 0 2 0 0 1 0 0 0 0 0 0
## 92000 0 0 3 0 0 0 0 0 0 0 0 0
## 93000 0 0 0 0 1 1 0 0 1 0 0 0
## 95000 1 1 2 2 0 1 0 0 0 0 0 0
## 96000 0 1 2 0 1 0 0 0 0 0 0 0
## 96500 0 0 1 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 1 1 0 0 0 0 0 0 0
## 98000 0 0 7 1 1 0 0 1 0 0 0 0
## 99000 0 0 0 0 0 1 0 0 0 0 0 0
## 100000 0 0 6 1 1 0 1 0 0 0 0 0
## 100400 0 0 0 1 0 0 0 0 0 0 0 0
## 101000 0 0 2 0 0 0 0 0 0 0 0 0
## 101100 0 0 0 0 0 0 0 0 1 0 0 0
## 101600 0 0 0 1 0 0 0 0 0 0 0 0
## 102500 0 0 0 0 0 0 1 0 0 0 0 0
## 103000 0 0 0 1 0 0 0 0 0 0 0 0
## 104000 0 0 0 0 2 0 0 0 0 0 0 0
## 105000 0 0 4 4 0 1 1 0 0 0 0 1
## 106000 0 0 0 0 0 0 2 0 1 0 0 0
## 107000 0 0 1 0 0 0 0 0 0 0 0 0
## 107300 0 0 1 0 0 0 0 0 0 0 0 0
## 107500 0 0 0 1 0 0 0 0 0 0 0 0
## 108000 0 0 0 1 1 0 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 1 0 0 0 0 0
## 112000 0 0 1 0 0 0 1 0 0 0 0 1
## 115000 0 2 0 1 2 0 0 0 0 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 1 0 0
## 120000 0 0 0 1 0 2 0 0 1 0 0 0
## 126710 0 0 0 1 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 1 0 0 0 0 0 0 0
## 145800 0 0 1 0 0 0 0 0 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 1 0
## 162000 0 1 0 0 0 0 0 0 0 0 0 0
## 220000 0 0 0 0 0 0 0 0 0 0 1 0margin.table(mytable,2)## work_yrs
## 0 1 2 3 4 5 6 7 8 10 15 16
## 1 8 38 21 11 7 7 1 4 1 2 2chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 535.23, df = 451, p-value = 0.003809p< 0.05 suggest that is some relationship between salary and prior work experiance
salary and gmat total
mytable <- xtabs(~ salary+gmat_tot,data= mba3.df)
mytable## gmat_tot
## salary 500 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660
## 64000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 77000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 78256 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 86000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 88000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 88500 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 90000 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0
## 92000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 93000 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0
## 95000 0 0 1 0 0 2 0 0 0 0 2 0 0 0 0 0
## 96000 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0
## 96500 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0
## 98000 0 0 0 0 0 1 3 1 1 0 1 0 0 0 0 0
## 99000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 100000 0 0 0 0 0 2 0 1 0 1 1 0 1 0 2 0
## 100400 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 101000 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
## 101100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 101600 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 102500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 104000 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
## 105000 0 0 0 0 2 0 2 3 0 1 0 1 0 0 1 0
## 106000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 107000 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 107300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 107500 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 108000 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 112000 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 115000 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 120000 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## 126710 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 145800 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 162000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## gmat_tot
## salary 670 680 700 710 720
## 64000 0 0 0 0 0
## 77000 0 0 0 0 0
## 78256 0 0 0 0 0
## 82000 1 0 0 0 0
## 85000 0 0 1 0 1
## 86000 0 1 0 0 0
## 88000 0 0 0 0 0
## 88500 0 0 0 0 0
## 90000 0 0 0 0 0
## 92000 0 0 0 1 0
## 93000 0 0 0 0 0
## 95000 2 0 0 0 0
## 96000 0 0 0 0 0
## 96500 0 0 0 0 0
## 97000 0 0 0 0 0
## 98000 1 1 0 1 0
## 99000 0 0 0 0 0
## 100000 0 0 0 1 0
## 100400 0 0 0 0 0
## 101000 0 0 0 0 0
## 101100 0 0 0 0 0
## 101600 0 0 0 0 0
## 102500 1 0 0 0 0
## 103000 0 0 0 0 0
## 104000 0 0 0 0 0
## 105000 0 1 0 0 0
## 106000 0 2 0 0 0
## 107000 0 0 0 0 0
## 107300 0 0 0 0 0
## 107500 0 0 0 0 0
## 108000 0 0 0 0 0
## 110000 0 0 0 0 0
## 112000 1 1 0 0 0
## 115000 0 0 0 1 0
## 118000 0 0 0 0 0
## 120000 1 0 1 0 0
## 126710 0 0 0 0 0
## 130000 0 0 0 0 0
## 145800 0 0 0 0 0
## 146000 0 0 0 0 0
## 162000 0 0 1 0 0
## 220000 0 0 0 0 0chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 927.24, df = 820, p-value = 0.005279p< 0.05 suggest that is some relationship between salary and gmat total score
salary and spring average
mytable <- xtabs(~ salary+s_avg,data= mba3.df)
mytable## s_avg
## salary 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.91 3 3.09 3.1 3.2 3.27 3.3 3.4
## 64000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 77000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 78256 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 82000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 85000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0
## 86000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 88000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 88500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 90000 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0
## 92000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1
## 93000 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1
## 95000 0 0 1 0 0 0 0 1 0 0 0 0 1 1 2 0
## 96000 0 0 0 1 0 0 0 0 0 0 0 0 1 0 2 0
## 96500 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 97000 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## 98000 0 1 0 0 0 1 1 4 0 1 0 0 2 0 0 0
## 99000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 100000 0 0 0 0 2 0 1 1 0 1 1 0 0 0 1 2
## 100400 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 101000 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 101100 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 101600 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 102500 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 104000 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
## 105000 1 0 0 0 0 0 0 0 1 2 0 0 1 0 2 0
## 106000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 107000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 107300 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 107500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 108000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112000 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 115000 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0
## 118000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 120000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 126710 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 145800 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 146000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 162000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## s_avg
## salary 3.45 3.5 3.6 3.7 3.8 4
## 64000 0 1 0 0 0 0
## 77000 0 0 0 0 0 0
## 78256 0 0 0 0 0 0
## 82000 0 0 0 0 0 0
## 85000 0 2 0 0 0 0
## 86000 0 1 0 0 0 0
## 88000 0 0 0 0 0 0
## 88500 0 0 0 0 0 0
## 90000 0 0 0 0 0 0
## 92000 0 0 0 0 0 0
## 93000 0 0 0 0 0 0
## 95000 0 0 1 0 0 0
## 96000 0 0 0 0 0 0
## 96500 0 0 0 0 0 0
## 97000 0 0 0 0 0 0
## 98000 0 0 0 0 0 0
## 99000 0 0 0 0 0 0
## 100000 0 0 0 0 0 0
## 100400 0 0 0 0 0 0
## 101000 0 0 0 0 0 0
## 101100 0 0 0 0 0 0
## 101600 0 0 0 0 0 0
## 102500 0 0 0 0 0 0
## 103000 0 0 0 0 0 0
## 104000 0 0 0 0 0 0
## 105000 1 1 1 0 1 0
## 106000 0 1 0 1 0 0
## 107000 0 0 0 0 0 0
## 107300 0 0 0 0 0 0
## 107500 0 0 0 0 0 0
## 108000 0 1 0 0 0 0
## 110000 0 0 1 0 0 0
## 112000 0 0 1 0 0 0
## 115000 0 0 1 1 0 0
## 118000 0 1 0 0 0 0
## 120000 0 2 0 0 1 0
## 126710 0 0 0 0 0 0
## 130000 0 0 0 0 0 0
## 145800 0 0 0 0 0 0
## 146000 0 0 0 0 0 1
## 162000 0 0 1 0 0 0
## 220000 0 0 0 0 0 0chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 792.97, df = 861, p-value = 0.9524p> 0.05 suggest that is no relationship between salary and spring mba average
mytable <- xtabs(~ salary+f_avg,data= mba3.df)
mytable## f_avg
## salary 0 2 2.25 2.5 2.67 2.75 2.83 3 3.25 3.33 3.5 3.6 3.67 3.75 4
## 64000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 77000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 78256 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 82000 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 85000 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0
## 86000 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
## 88000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 88500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 90000 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0
## 92000 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0
## 93000 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0
## 95000 0 0 0 0 0 1 0 1 2 0 2 0 1 0 0
## 96000 0 0 0 1 0 0 0 0 2 0 1 0 0 0 0
## 96500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 97000 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
## 98000 0 0 0 1 0 2 0 2 5 0 0 0 0 0 0
## 99000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 100000 0 0 0 0 0 1 0 5 1 0 1 0 1 0 0
## 100400 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 101000 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0
## 101100 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 101600 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 102500 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 103000 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 104000 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
## 105000 0 1 0 0 0 0 1 3 2 0 4 0 0 0 0
## 106000 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0
## 107000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 107300 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 107500 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 108000 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0
## 110000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 112000 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0
## 115000 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1
## 118000 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 120000 0 0 0 0 0 0 0 1 2 0 0 0 0 0 1
## 126710 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 130000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 145800 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 146000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 162000 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 220000 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 596.28, df = 574, p-value = 0.2518p> 0.05 suggest that is no relationship between salary and fall mba average
mytable <- xtabs(~ salary+satis,data= mba2.df)
mytable## satis
## salary 3 4 5 6 7
## 0 0 4 36 40 10
## 64000 0 0 0 0 1
## 77000 0 0 0 1 0
## 78256 0 0 1 0 0
## 82000 0 0 0 0 1
## 85000 0 0 1 3 0
## 86000 0 0 2 0 0
## 88000 0 0 0 0 1
## 88500 0 0 0 1 0
## 90000 0 0 2 0 1
## 92000 0 0 1 1 1
## 93000 0 0 1 2 0
## 95000 1 1 1 2 2
## 96000 0 0 1 1 2
## 96500 0 0 0 1 0
## 97000 0 0 0 1 1
## 98000 0 0 2 5 3
## 99000 0 0 0 1 0
## 100000 0 0 1 6 2
## 100400 0 0 0 0 1
## 101000 0 0 1 1 0
## 101100 0 0 0 1 0
## 101600 0 0 0 1 0
## 102500 0 0 1 0 0
## 103000 0 0 0 1 0
## 104000 0 0 1 1 0
## 105000 0 0 4 6 1
## 106000 0 0 0 2 1
## 107000 0 0 1 0 0
## 107300 0 0 0 0 1
## 107500 0 0 1 0 0
## 108000 0 0 0 2 0
## 110000 0 0 1 0 0
## 112000 0 0 0 2 1
## 115000 0 0 3 2 0
## 118000 0 0 0 0 1
## 120000 0 0 2 2 0
## 126710 0 0 0 1 0
## 130000 0 0 0 0 1
## 145800 0 0 0 1 0
## 146000 0 0 0 1 0
## 162000 0 0 1 0 0
## 220000 0 0 0 1 0chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 126.45, df = 168, p-value = 0.9928p> 0.05 suggest that is no relationship between salary and satisfaction
regression model
regr <- lm(salary ~ age+gmat_tot+gmat_qpc+gmat_vpc+gmat_tpc+s_avg+f_avg, data = mba3.df)
summary(regr)##
## Call:
## lm(formula = salary ~ age + gmat_tot + gmat_qpc + gmat_vpc +
## gmat_tpc + s_avg + f_avg, data = mba3.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31186 -7438 622 5299 69725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37730.6788 43445.3073 0.868 0.3873
## age 2657.3914 503.9286 5.273 8.39e-07 ***
## gmat_tot -0.8982 164.7408 -0.005 0.9957
## gmat_qpc 859.6496 480.8645 1.788 0.0770 .
## gmat_vpc 537.0116 480.7496 1.117 0.2668
## gmat_tpc -1454.7911 700.5012 -2.077 0.0405 *
## s_avg 4069.5906 4808.3606 0.846 0.3995
## f_avg -1827.8260 3750.0292 -0.487 0.6271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15290 on 95 degrees of freedom
## Multiple R-squared: 0.3181, Adjusted R-squared: 0.2679
## F-statistic: 6.332 on 7 and 95 DF, p-value: 4.124e-06Since the p value of f statistics is < 0.05 the model is correct. only age is significantly affecting salary.
regr <- lm(salary ~ ., data = mba2.df)
summary(regr)##
## Call:
## lm(formula = salary ~ ., data = mba2.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -93297 -48384 19635 43919 182830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 157118.8 109225.8 1.438 0.1520
## age -3827.0 1946.2 -1.966 0.0508 .
## sex 1249.6 8725.1 0.143 0.8863
## gmat_tot -281.8 314.3 -0.897 0.3711
## gmat_qpc 426.5 849.9 0.502 0.6164
## gmat_vpc 254.8 825.4 0.309 0.7579
## gmat_tpc 570.2 651.5 0.875 0.3826
## s_avg 8421.8 16684.6 0.505 0.6143
## f_avg -6198.0 8788.1 -0.705 0.4816
## quarter -6635.2 5147.0 -1.289 0.1990
## work_yrs 2704.3 2197.4 1.231 0.2201
## frstlang 14199.8 15677.4 0.906 0.3663
## satis 9830.8 5193.4 1.893 0.0600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52350 on 180 degrees of freedom
## Multiple R-squared: 0.09076, Adjusted R-squared: 0.03015
## F-statistic: 1.497 on 12 and 180 DF, p-value: 0.1285p value of f statistics is more so the model is rejected
for not placed students
salary and gender
t.test(mba4.df$salary, mba4.df$sex)##
## Welch Two Sample t-test
##
## data: mba4.df$salary and mba4.df$sex
## t = -27.156, df = 89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.347422 -1.163689
## sample estimates:
## mean of x mean of y
## 0.000000 1.255556p< 0.05 therefor it seems there is a relation
Salary and first language
t.test(mba4.df$salary, mba4.df$frstlang)##
## Welch Two Sample t-test
##
## data: mba4.df$salary and mba4.df$frstlang
## t = -36.097, df = 89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.148828 -1.028950
## sample estimates:
## mean of x mean of y
## 0.000000 1.088889p< 0.05 therefor it seems there is a relation
salary and work experiance
t.test(mba4.df$salary, mba4.df$work_yrs)##
## Welch Two Sample t-test
##
## data: mba4.df$salary and mba4.df$work_yrs
## t = -10.117, df = 89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.490102 -3.687675
## sample estimates:
## mean of x mean of y
## 0.000000 4.588889p< 0.05 therefor it seems there is a relation
salary and total gmat
t.test(mba4.df$salary, mba4.df$gmat_tot)##
## Welch Two Sample t-test
##
## data: mba4.df$salary and mba4.df$gmat_tot
## t = -92.723, df = 89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -627.4979 -601.1687
## sample estimates:
## mean of x mean of y
## 0.0000 614.3333