Please see my 2 previous asked questions if data is needed from there to answer this question. Run a regression of salary versus gender, age, prior experience, Beta experience, and any two of the education dummies, and interpret the results. Please include all inputs/ steps for excel Employee Gender Age Prior Experience Beta Experience Education Annual Salary 1 1 39 5 12 4 57700 2 0 44 12 8 6 76400 3 0 24 0 2 4 44000 4 1 25 2 1 4 41600 5 0 56 5 25 8 163900 6 1 41 9 10 4 72700 7 1 33 6 2 6 60300 8 0 37 11 6 4 63500 9 1 51 12 16 6 131200 10 0 23 0 1 4 39200 11 0 31 5 4 6 62900 12 1 27 0 8 0 26200 13 0 47 11 9 4 74500 14 1 35 5 5 6 64800 15 1 29 5 4 0 21600 16 0 46 4 15 6 81900 17 1 50 10 17 4 115400 18 0 30 3 6 4 57800 19 1 34 10 1 4 55800 20 1 42 11 8 4 76100 21 1 51 10 15 8 135700 22 0 63 16 20 4 140400 23 0 28 0 5 4 55400 24 1 32 4 1 4 49700 25 0 55 11 16 6 134800 26 1 45 20 2 4 76900 27 0 34 2 12 2 28700 28 0 33 2 7 4 58800 29 1 23 0 1 4 43100 30 0 40 4 13 6 82400 31 1 48 6 15 4 80100 32 1 27 0 6 0 27000 33 1 36 5 5 6 58800 34 0 58 9 22 4 133100 35 0 31 1 1 6 53700 36 1 21 0 1 2 26700 37 0 47 5 16 4 81300 38 1 35 3 7 4 55400 39 1 52 12 14 8 139900 40 0 29 3 3 2 33200 41 1 42 11 7 4 75000 42 0 60 10 21 4 128200 43 1 50 8 13 4 76800 44 1 33 1 2 6 54200 45 0 26 0 5 2 32600 46 0 38 6 6 6 59200 47 1 44 7 12 4 74800 48 0 25 0 3 4 45500 49 1 37 8 5 4 46500 50 0 53 13 13 6 136300 51 0 46 7 18 4 86900 52 1 20 0 1 0 23900 53 1 34 5 1 6 52700 54 1 60 12 13 4 92700 55 1 36 6 7 4 59500 56 0 41 6 3 6 69400 57 1 33 3 1 6 46600 58 0 29 3 8 4 61700 59 0 48 11 9 4 88200 60 1 43 0 4 6 45000 61 1 61 10 5 0 52200 62 0 30 5 1 6 61400 63 1 36 5 19 4 87500 64 1 48 7 23 4 103700 65 1 29 5 6 4 54000 66 0 26 11 23 4 125100 67 1 49 5 11 2 45900 68 0 28 10 2 6 79300 69 1 44 20 5 6 108600 70 1 48 0 13 6 68200 71 0 50 0 21 2 65200 72 1 48 12 14 4 95600 73 1 30 16 12 4 103100 74 1 41 20 23 4 143500 75 0 35 11 5 4 78200 76 1 28 3 3 4 40200 77 1 33 8 5 4 60500 78 1 61 0 7 4 40500 79 1 53 10 8 4 73800 80 1 48 4 4 4 45300 81 0 47 9 1 4 61400 82 1 48 4 7 6 64800 83 1 55 11 3 6 75600 84 0 32 1 19 6 95800 85 0 60 11 4 8 126700 86 0 50 10 2 4 67000 87 1 49 16 12 4 102600 88 0 22 4 3 4 52000 89 1 51 9 10 4 76000 90 1 22 0 3 8 83000 91 1 47 8 13 4 80800 92 1 41 10 10 6 91100 93 0 24 3 1 0 30100 94 1 64 5 7 4 55700 95 1 43 0 11 4 51400 96 0 22 3 1 4 43800 97 1 59 0 1 4 25000 98 0 32 10 15 2 80600 99 1 45 8 5 2 39600 100 0 47 0 1 2 13400 101 1 29 6 18 4 88200 102 0 61 9 15 6 109100 103 1 57 3 1 4 34200 104 1 65 4 9 4 57800 105 0 34 6 7 4 68100 106 0 54 6 13 6 94900 107 1 30 5 5 6 63200 108 1 39 6 16 4 82700 109 0 32 7 8 6 85600 110 1 24 2 7 2 27100 111 0 40 10 3 4 69800 112 0 52 13 4 4 81300 113 0 28 11 5 4 78400 114 0 53 20 9 6 127300 115 0 43 0 24 4 93700 116 0 30 5 6 6 74400 117 0 46 3 3 4 48300 118 1 38 10 13 6 98900 119 0 28 0 16 4 73300 120 1 46 11 19 6 117300 121 1 30 5 5 0 37800 122 1 43 6 14 4 77400 123 1 29 11 1 8 111200 124 0 48 11 4 4 75300 125 0 42 7 17 4 96900 126 0 18 10 19 6 123600 127 0 35 6 2 4 55200 128 1 22 0 1 0 12400 129 1 44 4 15 4 73900 130 1 47 20 4 4 94100 131 1 34 10 8 4 74300 132 1 37 11 4 4 66900 133 1 49 0 4 2 12500 134 0 32 0 18 6 90200 135 1 37 5 8 4 59000 136 1 29 10 19 6 114700 137 0 24 7 15 2 71700 138 0 43 20 18 0 125500 139 1 54 11 17 4 100200 140 1 26 0 4 6 45400 141 0 47 10 4 4 72200 142 1 31 5 12 4 69500 143 0 33 11 1 4 67900 144 0 42 2 7 6 67500 145 1 34 2 1 4 31800 146 1 59 0 10 2 27800 147 1 43 5 4 6 60200 148 1 30 2 2 4 34500 149 1 45 7 12 6 87000 150 1 50 0 4 2 12500 151 0 23 0 15 8 122700 152 1 44 5 7 4 56200 153 0 48 10 6 2 56900 154 1 47 4 12 4 66000 155 0 20 11 4 4 76000 156 1 31 0 16 2 44100 157 0 30 0 18 4 78500 158 1 42 5 13 4 71800 159 1 25 9 7 6 80700 160 1 24 2 15 2 47800 161 0 36 13 13 4 105000 162 0 32 6 15 6 100700 163 1 27 2 1 0 18300 164 0 55 12 12 6 110600 165 0 36 0 2 4 36800 166 1 22 0 4 6 45500 167 1 25 0 14 6 71400 168 1 47 5 14 4 74300 169 0 43 16 11 8 160600 170 1 53 0 7 6 52500 171 0 38 5 7 4 65000 172 0 39 12 14 4 104500 173 1 35 5 18 4 85000 174 1 23 3 10 8 110200 175 0 43 10 7 4 80100 176 1 33 3 3 4 40000 177 1 44 10 1 4 55900 178 1 33 0 16 4 64600 179 1 31 0 13 6 68600 180 1 36 7 8 4 65100 181 1 45 13 19 4 111700 182 1 45 12 1 4 62000 183 0 39 2 7 4 55800 184 0 45 5 11 2 54600 185 0 25 1 1 4 37600 186 1 34 0 7 4 41200 187 1 53 0 6 6 49900 188 0 35 4 6 4 59400 189 1 52 3 13 4 65500 190 1 33 10 3 6 73200 191 1 49 0 3 4 30500 192 1 59 6 17 4 84800 193 1 35 16 9 4 95200 194 1 44 11 11 4 84900 195 1 61 11 18 4 102600 196 1 43 11 1 4 59000 197 0 30 0 5 4 44800 198 0 32 11 2 4 70500 199 0 57 10 4 6 83700 200 1 44 10 18 4 100000 201 1 44 2 4 4 39300 202 1 45 0 7 2 20400 203 0 43 0 12 6 74300 204 0 33 11 19 4 114500
Please see my 2 previous asked questions if data is needed from there to answer this question. Run a regression of salary versus gender, age, prior experience, Beta experience, and any two of the education dummies, and interpret the results. Please include all inputs/ steps for excel Employee Gender Age Prior Experience Beta Experience Education Annual Salary 1 1 39 5 12 4 57700 2 0 44 12 8 6 76400 3 0 24 0 2 4 44000 4 1 25 2 1 4 41600 5 0 56 5 25 8 163900 6 1 41 9 10 4 72700 7 1 33 6 2 6 60300 8 0 37 11 6 4 63500 9 1 51 12 16 6 131200 10 0 23 0 1 4 39200 11 0 31 5 4 6 62900 12 1 27 0 8 0 26200 13 0 47 11 9 4 74500 14 1 35 5 5 6 64800 15 1 29 5 4 0 21600 16 0 46 4 15 6 81900 17 1 50 10 17 4 115400 18 0 30 3 6 4 57800 19 1 34 10 1 4 55800 20 1 42 11 8 4 76100 21 1 51 10 15 8 135700 22 0 63 16 20 4 140400 23 0 28 0 5 4 55400 24 1 32 4 1 4 49700 25 0 55 11 16 6 134800 26 1 45 20 2 4 76900 27 0 34 2 12 2 28700 28 0 33 2 7 4 58800 29 1 23 0 1 4 43100 30 0 40 4 13 6 82400 31 1 48 6 15 4 80100 32 1 27 0 6 0 27000 33 1 36 5 5 6 58800 34 0 58 9 22 4 133100 35 0 31 1 1 6 53700 36 1 21 0 1 2 26700 37 0 47 5 16 4 81300 38 1 35 3 7 4 55400 39 1 52 12 14 8 139900 40 0 29 3 3 2 33200 41 1 42 11 7 4 75000 42 0 60 10 21 4 128200 43 1 50 8 13 4 76800 44 1 33 1 2 6 54200 45 0 26 0 5 2 32600 46 0 38 6 6 6 59200 47 1 44 7 12 4 74800 48 0 25 0 3 4 45500 49 1 37 8 5 4 46500 50 0 53 13 13 6 136300 51 0 46 7 18 4 86900 52 1 20 0 1 0 23900 53 1 34 5 1 6 52700 54 1 60 12 13 4 92700 55 1 36 6 7 4 59500 56 0 41 6 3 6 69400 57 1 33 3 1 6 46600 58 0 29 3 8 4 61700 59 0 48 11 9 4 88200 60 1 43 0 4 6 45000 61 1 61 10 5 0 52200 62 0 30 5 1 6 61400 63 1 36 5 19 4 87500 64 1 48 7 23 4 103700 65 1 29 5 6 4 54000 66 0 26 11 23 4 125100 67 1 49 5 11 2 45900 68 0 28 10 2 6 79300 69 1 44 20 5 6 108600 70 1 48 0 13 6 68200 71 0 50 0 21 2 65200 72 1 48 12 14 4 95600 73 1 30 16 12 4 103100 74 1 41 20 23 4 143500 75 0 35 11 5 4 78200 76 1 28 3 3 4 40200 77 1 33 8 5 4 60500 78 1 61 0 7 4 40500 79 1 53 10 8 4 73800 80 1 48 4 4 4 45300 81 0 47 9 1 4 61400 82 1 48 4 7 6 64800 83 1 55 11 3 6 75600 84 0 32 1 19 6 95800 85 0 60 11 4 8 126700 86 0 50 10 2 4 67000 87 1 49 16 12 4 102600 88 0 22 4 3 4 52000 89 1 51 9 10 4 76000 90 1 22 0 3 8 83000 91 1 47 8 13 4 80800 92 1 41 10 10 6 91100 93 0 24 3 1 0 30100 94 1 64 5 7 4 55700 95 1 43 0 11 4 51400 96 0 22 3 1 4 43800 97 1 59 0 1 4 25000 98 0 32 10 15 2 80600 99 1 45 8 5 2 39600 100 0 47 0 1 2 13400 101 1 29 6 18 4 88200 102 0 61 9 15 6 109100 103 1 57 3 1 4 34200 104 1 65 4 9 4 57800 105 0 34 6 7 4 68100 106 0 54 6 13 6 94900 107 1 30 5 5 6 63200 108 1 39 6 16 4 82700 109 0 32 7 8 6 85600 110 1 24 2 7 2 27100 111 0 40 10 3 4 69800 112 0 52 13 4 4 81300 113 0 28 11 5 4 78400 114 0 53 20 9 6 127300 115 0 43 0 24 4 93700 116 0 30 5 6 6 74400 117 0 46 3 3 4 48300 118 1 38 10 13 6 98900 119 0 28 0 16 4 73300 120 1 46 11 19 6 117300 121 1 30 5 5 0 37800 122 1 43 6 14 4 77400 123 1 29 11 1 8 111200 124 0 48 11 4 4 75300 125 0 42 7 17 4 96900 126 0 18 10 19 6 123600 127 0 35 6 2 4 55200 128 1 22 0 1 0 12400 129 1 44 4 15 4 73900 130 1 47 20 4 4 94100 131 1 34 10 8 4 74300 132 1 37 11 4 4 66900 133 1 49 0 4 2 12500 134 0 32 0 18 6 90200 135 1 37 5 8 4 59000 136 1 29 10 19 6 114700 137 0 24 7 15 2 71700 138 0 43 20 18 0 125500 139 1 54 11 17 4 100200 140 1 26 0 4 6 45400 141 0 47 10 4 4 72200 142 1 31 5 12 4 69500 143 0 33 11 1 4 67900 144 0 42 2 7 6 67500 145 1 34 2 1 4 31800 146 1 59 0 10 2 27800 147 1 43 5 4 6 60200 148 1 30 2 2 4 34500 149 1 45 7 12 6 87000 150 1 50 0 4 2 12500 151 0 23 0 15 8 122700 152 1 44 5 7 4 56200 153 0 48 10 6 2 56900 154 1 47 4 12 4 66000 155 0 20 11 4 4 76000 156 1 31 0 16 2 44100 157 0 30 0 18 4 78500 158 1 42 5 13 4 71800 159 1 25 9 7 6 80700 160 1 24 2 15 2 47800 161 0 36 13 13 4 105000 162 0 32 6 15 6 100700 163 1 27 2 1 0 18300 164 0 55 12 12 6 110600 165 0 36 0 2 4 36800 166 1 22 0 4 6 45500 167 1 25 0 14 6 71400 168 1 47 5 14 4 74300 169 0 43 16 11 8 160600 170 1 53 0 7 6 52500 171 0 38 5 7 4 65000 172 0 39 12 14 4 104500 173 1 35 5 18 4 85000 174 1 23 3 10 8 110200 175 0 43 10 7 4 80100 176 1 33 3 3 4 40000 177 1 44 10 1 4 55900 178 1 33 0 16 4 64600 179 1 31 0 13 6 68600 180 1 36 7 8 4 65100 181 1 45 13 19 4 111700 182 1 45 12 1 4 62000 183 0 39 2 7 4 55800 184 0 45 5 11 2 54600 185 0 25 1 1 4 37600 186 1 34 0 7 4 41200 187 1 53 0 6 6 49900 188 0 35 4 6 4 59400 189 1 52 3 13 4 65500 190 1 33 10 3 6 73200 191 1 49 0 3 4 30500 192 1 59 6 17 4 84800 193 1 35 16 9 4 95200 194 1 44 11 11 4 84900 195 1 61 11 18 4 102600 196 1 43 11 1 4 59000 197 0 30 0 5 4 44800 198 0 32 11 2 4 70500 199 0 57 10 4 6 83700 200 1 44 10 18 4 100000 201 1 44 2 4 4 39300 202 1 45 0 7 2 20400 203 0 43 0 12 6 74300 204 0 33 11 19 4 114500
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Please see my 2 previous asked questions if data is needed from there to answer this question.
Run a regression of salary versus gender, age, prior experience, Beta experience, and any two of the education dummies, and interpret the results.
Please include all inputs/ steps for excel
| Employee | Gender | Age | Prior Experience | Beta Experience | Education | Annual Salary |
| 1 | 1 | 39 | 5 | 12 | 4 | 57700 |
| 2 | 0 | 44 | 12 | 8 | 6 | 76400 |
| 3 | 0 | 24 | 0 | 2 | 4 | 44000 |
| 4 | 1 | 25 | 2 | 1 | 4 | 41600 |
| 5 | 0 | 56 | 5 | 25 | 8 | 163900 |
| 6 | 1 | 41 | 9 | 10 | 4 | 72700 |
| 7 | 1 | 33 | 6 | 2 | 6 | 60300 |
| 8 | 0 | 37 | 11 | 6 | 4 | 63500 |
| 9 | 1 | 51 | 12 | 16 | 6 | 131200 |
| 10 | 0 | 23 | 0 | 1 | 4 | 39200 |
| 11 | 0 | 31 | 5 | 4 | 6 | 62900 |
| 12 | 1 | 27 | 0 | 8 | 0 | 26200 |
| 13 | 0 | 47 | 11 | 9 | 4 | 74500 |
| 14 | 1 | 35 | 5 | 5 | 6 | 64800 |
| 15 | 1 | 29 | 5 | 4 | 0 | 21600 |
| 16 | 0 | 46 | 4 | 15 | 6 | 81900 |
| 17 | 1 | 50 | 10 | 17 | 4 | 115400 |
| 18 | 0 | 30 | 3 | 6 | 4 | 57800 |
| 19 | 1 | 34 | 10 | 1 | 4 | 55800 |
| 20 | 1 | 42 | 11 | 8 | 4 | 76100 |
| 21 | 1 | 51 | 10 | 15 | 8 | 135700 |
| 22 | 0 | 63 | 16 | 20 | 4 | 140400 |
| 23 | 0 | 28 | 0 | 5 | 4 | 55400 |
| 24 | 1 | 32 | 4 | 1 | 4 | 49700 |
| 25 | 0 | 55 | 11 | 16 | 6 | 134800 |
| 26 | 1 | 45 | 20 | 2 | 4 | 76900 |
| 27 | 0 | 34 | 2 | 12 | 2 | 28700 |
| 28 | 0 | 33 | 2 | 7 | 4 | 58800 |
| 29 | 1 | 23 | 0 | 1 | 4 | 43100 |
| 30 | 0 | 40 | 4 | 13 | 6 | 82400 |
| 31 | 1 | 48 | 6 | 15 | 4 | 80100 |
| 32 | 1 | 27 | 0 | 6 | 0 | 27000 |
| 33 | 1 | 36 | 5 | 5 | 6 | 58800 |
| 34 | 0 | 58 | 9 | 22 | 4 | 133100 |
| 35 | 0 | 31 | 1 | 1 | 6 | 53700 |
| 36 | 1 | 21 | 0 | 1 | 2 | 26700 |
| 37 | 0 | 47 | 5 | 16 | 4 | 81300 |
| 38 | 1 | 35 | 3 | 7 | 4 | 55400 |
| 39 | 1 | 52 | 12 | 14 | 8 | 139900 |
| 40 | 0 | 29 | 3 | 3 | 2 | 33200 |
| 41 | 1 | 42 | 11 | 7 | 4 | 75000 |
| 42 | 0 | 60 | 10 | 21 | 4 | 128200 |
| 43 | 1 | 50 | 8 | 13 | 4 | 76800 |
| 44 | 1 | 33 | 1 | 2 | 6 | 54200 |
| 45 | 0 | 26 | 0 | 5 | 2 | 32600 |
| 46 | 0 | 38 | 6 | 6 | 6 | 59200 |
| 47 | 1 | 44 | 7 | 12 | 4 | 74800 |
| 48 | 0 | 25 | 0 | 3 | 4 | 45500 |
| 49 | 1 | 37 | 8 | 5 | 4 | 46500 |
| 50 | 0 | 53 | 13 | 13 | 6 | 136300 |
| 51 | 0 | 46 | 7 | 18 | 4 | 86900 |
| 52 | 1 | 20 | 0 | 1 | 0 | 23900 |
| 53 | 1 | 34 | 5 | 1 | 6 | 52700 |
| 54 | 1 | 60 | 12 | 13 | 4 | 92700 |
| 55 | 1 | 36 | 6 | 7 | 4 | 59500 |
| 56 | 0 | 41 | 6 | 3 | 6 | 69400 |
| 57 | 1 | 33 | 3 | 1 | 6 | 46600 |
| 58 | 0 | 29 | 3 | 8 | 4 | 61700 |
| 59 | 0 | 48 | 11 | 9 | 4 | 88200 |
| 60 | 1 | 43 | 0 | 4 | 6 | 45000 |
| 61 | 1 | 61 | 10 | 5 | 0 | 52200 |
| 62 | 0 | 30 | 5 | 1 | 6 | 61400 |
| 63 | 1 | 36 | 5 | 19 | 4 | 87500 |
| 64 | 1 | 48 | 7 | 23 | 4 | 103700 |
| 65 | 1 | 29 | 5 | 6 | 4 | 54000 |
| 66 | 0 | 26 | 11 | 23 | 4 | 125100 |
| 67 | 1 | 49 | 5 | 11 | 2 | 45900 |
| 68 | 0 | 28 | 10 | 2 | 6 | 79300 |
| 69 | 1 | 44 | 20 | 5 | 6 | 108600 |
| 70 | 1 | 48 | 0 | 13 | 6 | 68200 |
| 71 | 0 | 50 | 0 | 21 | 2 | 65200 |
| 72 | 1 | 48 | 12 | 14 | 4 | 95600 |
| 73 | 1 | 30 | 16 | 12 | 4 | 103100 |
| 74 | 1 | 41 | 20 | 23 | 4 | 143500 |
| 75 | 0 | 35 | 11 | 5 | 4 | 78200 |
| 76 | 1 | 28 | 3 | 3 | 4 | 40200 |
| 77 | 1 | 33 | 8 | 5 | 4 | 60500 |
| 78 | 1 | 61 | 0 | 7 | 4 | 40500 |
| 79 | 1 | 53 | 10 | 8 | 4 | 73800 |
| 80 | 1 | 48 | 4 | 4 | 4 | 45300 |
| 81 | 0 | 47 | 9 | 1 | 4 | 61400 |
| 82 | 1 | 48 | 4 | 7 | 6 | 64800 |
| 83 | 1 | 55 | 11 | 3 | 6 | 75600 |
| 84 | 0 | 32 | 1 | 19 | 6 | 95800 |
| 85 | 0 | 60 | 11 | 4 | 8 | 126700 |
| 86 | 0 | 50 | 10 | 2 | 4 | 67000 |
| 87 | 1 | 49 | 16 | 12 | 4 | 102600 |
| 88 | 0 | 22 | 4 | 3 | 4 | 52000 |
| 89 | 1 | 51 | 9 | 10 | 4 | 76000 |
| 90 | 1 | 22 | 0 | 3 | 8 | 83000 |
| 91 | 1 | 47 | 8 | 13 | 4 | 80800 |
| 92 | 1 | 41 | 10 | 10 | 6 | 91100 |
| 93 | 0 | 24 | 3 | 1 | 0 | 30100 |
| 94 | 1 | 64 | 5 | 7 | 4 | 55700 |
| 95 | 1 | 43 | 0 | 11 | 4 | 51400 |
| 96 | 0 | 22 | 3 | 1 | 4 | 43800 |
| 97 | 1 | 59 | 0 | 1 | 4 | 25000 |
| 98 | 0 | 32 | 10 | 15 | 2 | 80600 |
| 99 | 1 | 45 | 8 | 5 | 2 | 39600 |
| 100 | 0 | 47 | 0 | 1 | 2 | 13400 |
| 101 | 1 | 29 | 6 | 18 | 4 | 88200 |
| 102 | 0 | 61 | 9 | 15 | 6 | 109100 |
| 103 | 1 | 57 | 3 | 1 | 4 | 34200 |
| 104 | 1 | 65 | 4 | 9 | 4 | 57800 |
| 105 | 0 | 34 | 6 | 7 | 4 | 68100 |
| 106 | 0 | 54 | 6 | 13 | 6 | 94900 |
| 107 | 1 | 30 | 5 | 5 | 6 | 63200 |
| 108 | 1 | 39 | 6 | 16 | 4 | 82700 |
| 109 | 0 | 32 | 7 | 8 | 6 | 85600 |
| 110 | 1 | 24 | 2 | 7 | 2 | 27100 |
| 111 | 0 | 40 | 10 | 3 | 4 | 69800 |
| 112 | 0 | 52 | 13 | 4 | 4 | 81300 |
| 113 | 0 | 28 | 11 | 5 | 4 | 78400 |
| 114 | 0 | 53 | 20 | 9 | 6 | 127300 |
| 115 | 0 | 43 | 0 | 24 | 4 | 93700 |
| 116 | 0 | 30 | 5 | 6 | 6 | 74400 |
| 117 | 0 | 46 | 3 | 3 | 4 | 48300 |
| 118 | 1 | 38 | 10 | 13 | 6 | 98900 |
| 119 | 0 | 28 | 0 | 16 | 4 | 73300 |
| 120 | 1 | 46 | 11 | 19 | 6 | 117300 |
| 121 | 1 | 30 | 5 | 5 | 0 | 37800 |
| 122 | 1 | 43 | 6 | 14 | 4 | 77400 |
| 123 | 1 | 29 | 11 | 1 | 8 | 111200 |
| 124 | 0 | 48 | 11 | 4 | 4 | 75300 |
| 125 | 0 | 42 | 7 | 17 | 4 | 96900 |
| 126 | 0 | 18 | 10 | 19 | 6 | 123600 |
| 127 | 0 | 35 | 6 | 2 | 4 | 55200 |
| 128 | 1 | 22 | 0 | 1 | 0 | 12400 |
| 129 | 1 | 44 | 4 | 15 | 4 | 73900 |
| 130 | 1 | 47 | 20 | 4 | 4 | 94100 |
| 131 | 1 | 34 | 10 | 8 | 4 | 74300 |
| 132 | 1 | 37 | 11 | 4 | 4 | 66900 |
| 133 | 1 | 49 | 0 | 4 | 2 | 12500 |
| 134 | 0 | 32 | 0 | 18 | 6 | 90200 |
| 135 | 1 | 37 | 5 | 8 | 4 | 59000 |
| 136 | 1 | 29 | 10 | 19 | 6 | 114700 |
| 137 | 0 | 24 | 7 | 15 | 2 | 71700 |
| 138 | 0 | 43 | 20 | 18 | 0 | 125500 |
| 139 | 1 | 54 | 11 | 17 | 4 | 100200 |
| 140 | 1 | 26 | 0 | 4 | 6 | 45400 |
| 141 | 0 | 47 | 10 | 4 | 4 | 72200 |
| 142 | 1 | 31 | 5 | 12 | 4 | 69500 |
| 143 | 0 | 33 | 11 | 1 | 4 | 67900 |
| 144 | 0 | 42 | 2 | 7 | 6 | 67500 |
| 145 | 1 | 34 | 2 | 1 | 4 | 31800 |
| 146 | 1 | 59 | 0 | 10 | 2 | 27800 |
| 147 | 1 | 43 | 5 | 4 | 6 | 60200 |
| 148 | 1 | 30 | 2 | 2 | 4 | 34500 |
| 149 | 1 | 45 | 7 | 12 | 6 | 87000 |
| 150 | 1 | 50 | 0 | 4 | 2 | 12500 |
| 151 | 0 | 23 | 0 | 15 | 8 | 122700 |
| 152 | 1 | 44 | 5 | 7 | 4 | 56200 |
| 153 | 0 | 48 | 10 | 6 | 2 | 56900 |
| 154 | 1 | 47 | 4 | 12 | 4 | 66000 |
| 155 | 0 | 20 | 11 | 4 | 4 | 76000 |
| 156 | 1 | 31 | 0 | 16 | 2 | 44100 |
| 157 | 0 | 30 | 0 | 18 | 4 | 78500 |
| 158 | 1 | 42 | 5 | 13 | 4 | 71800 |
| 159 | 1 | 25 | 9 | 7 | 6 | 80700 |
| 160 | 1 | 24 | 2 | 15 | 2 | 47800 |
| 161 | 0 | 36 | 13 | 13 | 4 | 105000 |
| 162 | 0 | 32 | 6 | 15 | 6 | 100700 |
| 163 | 1 | 27 | 2 | 1 | 0 | 18300 |
| 164 | 0 | 55 | 12 | 12 | 6 | 110600 |
| 165 | 0 | 36 | 0 | 2 | 4 | 36800 |
| 166 | 1 | 22 | 0 | 4 | 6 | 45500 |
| 167 | 1 | 25 | 0 | 14 | 6 | 71400 |
| 168 | 1 | 47 | 5 | 14 | 4 | 74300 |
| 169 | 0 | 43 | 16 | 11 | 8 | 160600 |
| 170 | 1 | 53 | 0 | 7 | 6 | 52500 |
| 171 | 0 | 38 | 5 | 7 | 4 | 65000 |
| 172 | 0 | 39 | 12 | 14 | 4 | 104500 |
| 173 | 1 | 35 | 5 | 18 | 4 | 85000 |
| 174 | 1 | 23 | 3 | 10 | 8 | 110200 |
| 175 | 0 | 43 | 10 | 7 | 4 | 80100 |
| 176 | 1 | 33 | 3 | 3 | 4 | 40000 |
| 177 | 1 | 44 | 10 | 1 | 4 | 55900 |
| 178 | 1 | 33 | 0 | 16 | 4 | 64600 |
| 179 | 1 | 31 | 0 | 13 | 6 | 68600 |
| 180 | 1 | 36 | 7 | 8 | 4 | 65100 |
| 181 | 1 | 45 | 13 | 19 | 4 | 111700 |
| 182 | 1 | 45 | 12 | 1 | 4 | 62000 |
| 183 | 0 | 39 | 2 | 7 | 4 | 55800 |
| 184 | 0 | 45 | 5 | 11 | 2 | 54600 |
| 185 | 0 | 25 | 1 | 1 | 4 | 37600 |
| 186 | 1 | 34 | 0 | 7 | 4 | 41200 |
| 187 | 1 | 53 | 0 | 6 | 6 | 49900 |
| 188 | 0 | 35 | 4 | 6 | 4 | 59400 |
| 189 | 1 | 52 | 3 | 13 | 4 | 65500 |
| 190 | 1 | 33 | 10 | 3 | 6 | 73200 |
| 191 | 1 | 49 | 0 | 3 | 4 | 30500 |
| 192 | 1 | 59 | 6 | 17 | 4 | 84800 |
| 193 | 1 | 35 | 16 | 9 | 4 | 95200 |
| 194 | 1 | 44 | 11 | 11 | 4 | 84900 |
| 195 | 1 | 61 | 11 | 18 | 4 | 102600 |
| 196 | 1 | 43 | 11 | 1 | 4 | 59000 |
| 197 | 0 | 30 | 0 | 5 | 4 | 44800 |
| 198 | 0 | 32 | 11 | 2 | 4 | 70500 |
| 199 | 0 | 57 | 10 | 4 | 6 | 83700 |
| 200 | 1 | 44 | 10 | 18 | 4 | 100000 |
| 201 | 1 | 44 | 2 | 4 | 4 | 39300 |
| 202 | 1 | 45 | 0 | 7 | 2 | 20400 |
| 203 | 0 | 43 | 0 | 12 | 6 | 74300 |
| 204 | 0 | 33 | 11 | 19 | 4 | 114500 |
Expert Solution
Step 1
Use the given data to perform regression analysis using excel.
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.963350388 | |||||
| R Square | 0.92804397 | |||||
| Adjusted R Square | 0.926226899 | |||||
| Standard Error | 8216.953361 | |||||
| Observations | 204 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 5 | 172420219587.90 | 34484043918 | 510.7360879 | 0.000 | |
| Residual | 198 | 13368627863 | 67518322.54 | |||
| Total | 203 | 185788847450.98 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 5429.210042 | 2598.1238 | 2.089665643 | 0.037925202 | 305.664498 | 10552.75559 |
| Gender | -7061.072496 | 1181.879286 | -5.974444748 | 0.0000000106 | -9391.759105 | -4730.385886 |
| Age | -101.8252792 | 57.71421278 | -1.764301622 | 0.079223184 | -215.6387155 | 11.9881571 |
| Prior Experience | 3074.188348 | 123.7685251 | 24.83820782 | 0.0000 | 2830.114653 | 3318.262043 |
| Beta Experience | 2600.999738 | 97.52797528 | 26.66926828 | 0.0000 | 2408.672866 | 2793.326609 |
| Education | 7413.197496 | 350.9650588 | 21.12232346 | 0.0000 | 6721.088266 | 8105.306726 |
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Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman