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Posted on Jul 19, 2024

HT 0.491 SALARY YRS 3750 3330 3100 3000 2750 2500 2500 2500 2200 2140 2140 2100 2100 2100 2100 2100 2100 2000 2100 1800 1800

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HT 0.491 SALARY YRS 3750 3330 3100 3000 2750 2500 2500 2500 2200 2140 2140 2100 2100 2100 2100 2100 2100 2000 2100 1800 1800 1700 1700 1700 1500 1500 1500 1500 1500 1330 1320 1300 1300 1300 1200 1200 1200 1200 1200 1200 1100 1100 1100 1100 1100 1100 1100 1100 1050 1000 1000 1000 1000 1000 1000 1000 4 4 10 6 10 6 6 4 4 2 4 8 7 13 3 6 13 13 1 3 8 7 9 7 8 6 7 11 4 4 10 7 8 5 8 8 12 9 8 5 4 2 5 3 6 11 WT 84 79 81 78 81 78 84 81 84 74 75 73 80 82 84 88 79 84 82 81 82 81 83 75 83 79 81 79 81 75 85 88 80 82 84 85 83 82 78 85 84 75 83 85 76 82 81 79 73 81 76 83 73 82 81 78 AGE 240 215 222 253 220 198 250 250 260 180 195 185 200 255 280 230 190 230 230 254 230 235 260 175 260 215 225 215 230 190 245 290 230 235 240 255 255 225 200 240 255 205 235 255 195 220 245 215 175 245 205 232 175 217 220 220 MIN FGPRCNT REBOUND POINTS AVGPNTS WINTM ALLSTAR 27 2896 0.567 240 1815 22.7 1 1 26 3093 0.509 483 2176 26.5 0 1 30 2886 0.509 607 1730 22.5 1 1 26 3088 0.579 986 2037 25.8 1 1 33 189 0.471 37 116 19.3 0 1 26 3255 0.538 652 2633 32.5 1 1 26 3024 0.508 1105 2039 24.8 1 1 25 3000 0.523 299 613 19.8 0 0 26 1531 0.524 453 345 4.7 1 0 23 3179 0.505 340 1650 20.4 1 1 26 3102 0.471 367 1431 17.7 0 0 28 2924 0.464 273 1458 18.2 1 1 29 2997 0.464 553 2099 26.2 1 1 34 2878 0.491 956 1637 20.2 1 0 25 2662 0.477 635 1432 18.1 0 0 29 1086 0.449 307 393 6.4 0 35 2990 326 2175 26.5 0 36 2840 0.57 996 1486 18.6 1 24 950 0.494 171 434 16.7 0 26 3126 0.519 853 2326 29.1 0 30 3002 0.471 684 2085 25.7 1 28 2824 0.467 650 1829 22.9 0 30 2739 0.477 769 1409 17.2 0 29 2745 0.457 662 1409 19.8 1 30 1917 0.522 500 969 12.8 0 29 3190 0.501 342 2253 27.5 0 28 2960 0.548 489 1657 20.5 1 33 2559 0.477 384 1674 20.7 0 26 2510 0.529 739 1088 13.3 1 26 2409 0.505 172 1186 17.2 0 32 2333 0.475 521 966 12.4 0 32 2914 0.462 50B 6.2 0 29 2446 0.531 959 13 0 27 1633 0.459 413 671 10.2 0 31 1806 0.499 545 657 8 0 31 1996 0.448 473 902 14.1 0 34 2587 0.431 623 1068 13.4 0 32 2876 0.546 637 1758 22.5 1 30 2946 0.476 273 1534 19.7 1 28 412 0.451 106 171 8.6 0 25 2585 0.541 696 1299 16.4 0 24 2477 0.467 341 1219 16.9 1 0 27 3135 0.542 787 1370 16.7 1 0 25 2821 0.538 1475 18.9 1 0 28 2605 0.491 302 1448 19.6 1 0 33 1865 0.495 619 520 6.6 0 25 2120 0.503 541 1176 15.9 0 27 3006 0.496 615 2123 27.2 1 27 3171 0.538 248 1400 17.1 1 1 26 2604 861 1061 1 0 28 2418 0.48 1651 0 28 2777 0.483 447 1595 19.5 0 25 2728 0.526 226 1414 18.9 0 30 2526 0.539 581 1259 17.2 0 0 28 2542 0.482 522 1308 16 0 30 2302 0.467 267 1606 22.6 0 OOOOOOOOOOOOOOOO 843 696 718 4 0.51 12.9 21.7 179 Down OOOOOO 1) Use the data in 7-20 and run the regression below in Gretl. Make sure you open the data as a time- series. (I know this is a cross section, but as stated in class, there might be serial correlation based on how the data was organized.) salary = a + Baheight+ B2 weight+ 3 points + E 1. Run the regression and print out the regression result and include a copy on the homework. Check the Durbin-Watson statistic. Do you have serial correlation? Which kind? 2. Run a White's test to check for heteroskedasticity. Based on the White's Test, does your model have heteroskedasticity? 3. Print out a copy of the White's test and the Durbin Watson test result and attach it in your homework. Handwritten DW tests and White's Test results will not be accepted. HT 0.491 SALARY YRS 3750 3330 3100 3000 2750 2500 2500 2500 2200 2140 2140 2100 2100 2100 2100 2100 2100 2000 2100 1800 1800 1700 1700 1700 1500 1500 1500 1500 1500 1330 1320 1300 1300 1300 1200 1200 1200 1200 1200 1200 1100 1100 1100 1100 1100 1100 1100 1100 1050 1000 1000 1000 1000 1000 1000 1000 4 4 10 6 10 6 6 4 4 2 4 8 7 13 3 6 13 13 1 3 8 7 9 7 8 6 7 11 4 4 10 7 8 5 8 8 12 9 8 5 4 2 5 3 6 11 WT 84 79 81 78 81 78 84 81 84 74 75 73 80 82 84 88 79 84 82 81 82 81 83 75 83 79 81 79 81 75 85 88 80 82 84 85 83 82 78 85 84 75 83 85 76 82 81 79 73 81 76 83 73 82 81 78 AGE 240 215 222 253 220 198 250 250 260 180 195 185 200 255 280 230 190 230 230 254 230 235 260 175 260 215 225 215 230 190 245 290 230 235 240 255 255 225 200 240 255 205 235 255 195 220 245 215 175 245 205 232 175 217 220 220 MIN FGPRCNT REBOUND POINTS AVGPNTS WINTM ALLSTAR 27 2896 0.567 240 1815 22.7 1 1 26 3093 0.509 483 2176 26.5 0 1 30 2886 0.509 607 1730 22.5 1 1 26 3088 0.579 986 2037 25.8 1 1 33 189 0.471 37 116 19.3 0 1 26 3255 0.538 652 2633 32.5 1 1 26 3024 0.508 1105 2039 24.8 1 1 25 3000 0.523 299 613 19.8 0 0 26 1531 0.524 453 345 4.7 1 0 23 3179 0.505 340 1650 20.4 1 1 26 3102 0.471 367 1431 17.7 0 0 28 2924 0.464 273 1458 18.2 1 1 29 2997 0.464 553 2099 26.2 1 1 34 2878 0.491 956 1637 20.2 1 0 25 2662 0.477 635 1432 18.1 0 0 29 1086 0.449 307 393 6.4 0 35 2990 326 2175 26.5 0 36 2840 0.57 996 1486 18.6 1 24 950 0.494 171 434 16.7 0 26 3126 0.519 853 2326 29.1 0 30 3002 0.471 684 2085 25.7 1 28 2824 0.467 650 1829 22.9 0 30 2739 0.477 769 1409 17.2 0 29 2745 0.457 662 1409 19.8 1 30 1917 0.522 500 969 12.8 0 29 3190 0.501 342 2253 27.5 0 28 2960 0.548 489 1657 20.5 1 33 2559 0.477 384 1674 20.7 0 26 2510 0.529 739 1088 13.3 1 26 2409 0.505 172 1186 17.2 0 32 2333 0.475 521 966 12.4 0 32 2914 0.462 50B 6.2 0 29 2446 0.531 959 13 0 27 1633 0.459 413 671 10.2 0 31 1806 0.499 545 657 8 0 31 1996 0.448 473 902 14.1 0 34 2587 0.431 623 1068 13.4 0 32 2876 0.546 637 1758 22.5 1 30 2946 0.476 273 1534 19.7 1 28 412 0.451 106 171 8.6 0 25 2585 0.541 696 1299 16.4 0 24 2477 0.467 341 1219 16.9 1 0 27 3135 0.542 787 1370 16.7 1 0 25 2821 0.538 1475 18.9 1 0 28 2605 0.491 302 1448 19.6 1 0 33 1865 0.495 619 520 6.6 0 25 2120 0.503 541 1176 15.9 0 27 3006 0.496 615 2123 27.2 1 27 3171 0.538 248 1400 17.1 1 1 26 2604 861 1061 1 0 28 2418 0.48 1651 0 28 2777 0.483 447 1595 19.5 0 25 2728 0.526 226 1414 18.9 0 30 2526 0.539 581 1259 17.2 0 0 28 2542 0.482 522 1308 16 0 30 2302 0.467 267 1606 22.6 0 OOOOOOOOOOOOOOOO 843 696 718 4 0.51 12.9 21.7 179 Down OOOOOO 1) Use the data in 7-20 and run the regression below in Gretl. Make sure you open the data as a time- series. (I know this is a cross section, but as stated in class, there might be serial correlation based on how the data was organized.) salary = a + Baheight+ B2 weight+ 3 points + E 1. Run the regression and print out the regression result and include a copy on the homework. Check the Durbin-Watson statistic. Do you have serial correlation? Which kind? 2. Run a White's test to check for heteroskedasticity. Based on the White's Test, does your model have heteroskedasticity? 3. Print out a copy of the White's test and the Durbin Watson test result and attach it in your homework. Handwritten DW tests and White's Test results will not be accepted