Multiple Linear Regression Analysis (MLR)
Perform two different MLRs of your dependent variable against a selection of your independent variables, and use the Partial F-test to compare the two models. On the basis of this test, which of the two models is the preferred one? Comment on why this is the preferred model.
Given the correlations among the independent variables that you found earlier, are there any reasons to believe that we may have substantial multicollinearity? What does this mean? Comment on this in your Word document and, if necessary, undertake a remedy.
M P H N R U K 0 Q S T A B C D E F G Ex1 LF M N U1 U2 W X Variable Definitions R Age Ed EXO 261 R: Crime rate: # of offenses reported to police per million population 91 58 56 510 950 33 108 41 394 79.1 151 557 194 Age: The number of males of age 14-24 per 1000 population 113 103 95 583 1012 96 36 163.5 143 318 250 S: Indicator variable for Southern states (0 = No, 1 = Yes) UAW NA 533 33 142 89 45 44 969 18 94 57.8 994 157 102 39 673 167 Ed: Mean # of years of schooling x 10 for persons of age 25 or older 121 149 141 577 196.9 136 174 EXO: 1960 per capita expenditure on police by state and local government 121 109 101 591 985 18 91 20 578 123.4 141 547 964 25 84 29 689 126 Ex1: 1959 per capita expenditure on police by state and local government 68.2 121 110 118 115 519 982 4 97 38 620 168 LF: Labor force participation rate per 1000 civilian urban males age 14-24 96.3 127 111 32 79 79 35 472 206 M: The number of males per 1000 females 155.5 131 109 115 109 542 969 50 81 28 421 239 N: State population size in hundred thousands OHH OOOO OHH HOOOHOHV 65 62 553 955 39 10 85.6 157 90 24 526 174 U1: Unemployment rate of urban males per 1000 of age 14-24 71 68 632 1029 100 11 70.5 140 118 170 U2: Unemployment rate of urban males per 1000 of age 35-39 580 966 101 77 35 657 167.4 124 105 121 116 31 580 172 W: Median value of transferable goods and assets or family income in tens of $ 12 13 134 108 75 71 595 972 47 83 84.9 113 624 206 X: The number of families per 1000 earning below 1/2 the median income 67 60 972 28 77 25 507 14 51.1 128 22 77 27 529 190 15 66.4 135 117 62 61 595 986 53 530 986 30 92 43 405 264 16 79.8 152 87 57 88 81 77 497 956 33 116 47 427 247 17 94.6 142 166 143 110 56 63 537 977 10 114 35 487 18 53.9 537 978 31 89 34 631 165 19 92.9 135 104 123 115 128 536 51 78 34 135 934 627 20 75 130 116 128 567 78 130 58 626 166 985 21 125 113 105 122.5 108 33 557 195 22 74.2 126 108 74 67 602 984 34 102 34 288 276 23 43.9 157 89 47 44 512 962 22 97 227 96 87 83 564 953 43 83 32 513 24 121.6 132 142 42 540 176 25 96.8 131 116 78 73 574 1038 486 196 52.3 130 116 63 57 641 984 14 70 21 26 143 631 1071 102 41 674 152 27 199.3 131 121 160 139 109 69 71 540 965 80 22 564 28 34.2 135 76 571 1018 10 103 28 537 215 29 121.6 152 112 32 521 938 168 92 36 637 154 30 104.3 119 107 166 157 72 26 396 237 31 69.6 166 89 58 54 521 973 46 40 453 200 32 37.3 140 93 55 54 535 1045 135 OH H O H O OOH OOH OO OO OOO 81 586 964 97 105 43 617 163 33 75.4 125 109 90 560 972 23 76 24 462 233 34 107.2 147 104 53 64 102 35 589 166 35 92.3 126 118 97 97 542 990 18 158 102 97 87 526 948 113 124 50 572 36 65.3 123 9 87 38 559 153 100 109 98 531 964 37 127.2 150 24 76 28 382 254 38 83.1 177 87 58 56 638 974 27 425 225 39 56.6 133 104 51 47 599 1024 7 99 88 61 54 515 953 36 86 35 395 251 40 82.6 149 560 981 36 88 31 488 228 41 145 104 74 115.1 82 601 998 84 20 590 144 148 122 72 99 42 88 489 170 54.2 141 109 56 54 523 968 107 37 43 224 99 75 70 522 996 40 73 27 496 44 82.3 162 574 1012 29 111 37 622 162 103 95 96 45 136 OHOHO 121 46 45.5 139 88 46 41 480 968 19 135 53 457 249 97 599 989 40 78 25 593 171 47 50.8 126 104 106 113 588 130 91 623 40 160 48 84.9 121 1049
