Lab042122 - The data are a random sample of records of...

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Unformatted text preview: The data are a random sample of records of resales of homes from Feb 15 to Apr 30, 1993 from the files maintained by the Albuquerque Board of Realtors. 1. PRICE = Selling price ($hundreds) 2. SQFT = Square feet of living space 3. AGE = Age of home (years) 4. FEATS = Number out of 11 features (dishwasher, refrigerator, microwave, disposer, washer, intercom, skylight(s), compactor, dryer, handicap fit, cable TV access) 5. NE = Located in northeast sector of city (1) or not (0) 6. COR = Corner location (1) or not (0) 7. TAX = Annual taxes ($) We want to use this data to find an expected selling price for a home with given characteristics. 1. Set up linear regression model using all the 7 variables. 2. Check the multicollinearity. 3. Reset up linear regression model considering the multicollinearity problem. 4. Test the residuals for normality. 5. Test the residuals for heteroscedasticity. 6. Check whether the data set includes outlier(s). Apr 30, 1993 PRICE 2050 2150 2150 1999 1900 1800 1560 1449 1375 1270 1250 1235 1170 1155 1110 1139 995 900 960 1695 1553 1020 1020 850 720 749 2150 1350 1299 1250 1239 1125 1080 1050 1049 934 875 805 759 729 710 975 939 2100 580 SQRT 2650 2664 2921 2580 2580 2774 1920 1710 1837 1880 2150 1894 1928 1767 1630 1680 1500 1400 1573 2931 2200 1478 1713 1190 1121 1733 2848 2253 2743 2180 1706 1710 2200 1680 1900 1543 1173 1258 997 1007 1083 1500 1428 2116 1051 AGE 13 6 3 4 4 2 1 1 4 8 15 14 18 16 15 17 15 16 17 28 28 53 30 41 46 43 4 23 25 17 14 16 26 13 34 20 6 7 4 19 22 7 40 25 15 en characteristics. 1844 699 1160 1109 1129 1050 1045 1050 1020 1000 1030 975 940 920 945 874 872 870 869 766 739 2250 1400 1720 1740 1700 1620 1630 1920 1606 1535 1540 1739 1305 1415 1580 1236 1229 1273 1165 1200 970 40 45 5 4 6 6 6 8 5 7 6 13 5 7 9 3 6 4 7 7 4 FEATS 7 5 6 4 4 4 5 3 5 6 3 5 8 4 3 4 4 2 6 3 4 3 4 1 4 6 6 4 5 4 4 4 4 4 3 3 4 4 4 6 4 3 2 3 2 NE 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 COR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 TAX 1639 1193 1635 1732 1534 1765 1161 1010 1191 930 984 1112 600 794 867 750 743 731 768 1142 1035 626 600 600 398 656 1487 939 1232 1141 810 800 1076 875 690 820 456 821 461 513 504 700 701 1209 426 6 1 4 3 4 4 4 4 4 5 2 3 3 4 3 4 3 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 915 481 867 816 725 800 750 944 811 668 826 880 647 866 810 707 721 638 694 634 541 A researcher want to study the effect of brain size and some other personal chracteristics on IQ score. The researchers (Willerman et al. 1991) used Magnetic Resonance Imaging (MRI) to determine the brain size 1. FSIQ: 2. MRI_Count: 3. Female: 4. Weight: 5. Height: full Scale IQ scores based on the four Wechsler (1981) subtests total pixel Count from the 18 MRI scans 1 if female; 0 otherwise body weight in pounds height in inches 1. Set up linear regression model using all the 5 variables. 2. Check the multicollinearity. 3. Test the residuals for normality. 4. Test the residuals for heteroscedasticity. 5. Check whether the data set includes outlier(s). eristics on IQ score. o determine the brain size of the subjects. FSIQ 133 139 133 137 99 138 92 89 133 132 141 135 140 96 83 132 100 101 80 97 135 139 91 141 85 103 77 130 133 144 103 90 83 133 140 88 81 89 MRI_Count Female 816932 1 1038437 0 965353 0 951545 1 928799 1 991305 1 854258 1 904858 0 955466 0 833868 1 1079549 0 924059 0 856472 1 878897 1 865363 1 852244 1 945088 0 808020 1 889083 0 905940 0 790619 1 955003 0 831772 1 935494 0 798612 1 1062462 0 793549 1 866662 1 857782 1 949589 0 997925 0 879987 0 834344 1 948066 1 949395 0 893983 1 930016 0 935863 0 Weight 118 143 172 147 146 138 175 134 172 118 151 155 155 146 135 127 178 136 180 186 122 132 114 171 140 187 106 159 127 191 192 181 143 153 144 139 148 179 Height 64.5 73.3 68.8 65 69 64.5 66 66.3 68.8 64.5 70 69 70.5 66 68 68.5 73.5 66.3 70 76.5 62 68 63 72 68 77 63 66.5 62.5 67 75.5 69 66.5 66.5 70.5 64.5 74 75.5 The data are based on a 5% sample of all births occurring in Philadelphia in 1990, and the variables are gram black educ smoke gestate : : : : : Birth weight in grams Mother is black (1=yes, 0=no) Mother's years of education (0,17) Whether mother smoked during pregnancy (1=yes, 0=no), Gestational age in weeks grams 2898 994 3977 3040 3523 3100 3670 3097 3040 3239 2955 2200 3182 3510 3381 3530 2985 3374 3765 2715 3640 3040 3670 2775 2680 2699 2557 3835 3381 2766 3125 2557 2950 3779 2760 black2 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 educ 0 0 2 2 2 5 6 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 smoke2 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 gestate 40 26 38 37 38 40 40 41 39 39 38 38 40 40 39 40 38 39 42 39 39 42 39 38 38 37 38 40 41 36 41 38 38 40 41 3011 2415 3125 3154 3170 2784 2475 2642 4034 3988 3370 2955 2443 4000 3630 2920 2727 3800 3771 3075 2415 2890 3892 3125 3892 3296 3120 1710 3068 3779 2898 3381 3324 2727 2642 2244 2528 3352 3551 3267 3210 3395 3068 3420 2985 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 1 1 0 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 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This note was uploaded on 07/01/2011 for the course ECO 370 taught by Professor Camp during the Spring '11 term at Indiana State University .

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