Lab042122A - 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, 19 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 charact 1. Set up linear regression model using all the 7 variables. PRICE = beta0 + beta1*SQFT + beta2*AGE + beta3*FEATS + beta4*NE + beta5*COR + be 2. Check the multicollinearity. SQRT AGE FEATS NE SQRT 1 AGE-0.04 1 FEATS 0.36-0.18 1 NE 0.36 0.22 0.31 1 COR-0.08 0.16-0.25-0.02 TAX 0.88-0.29 0.3 0.3 The correlation coefficient between SQRT and TAX is higher than 0.8. So, we remove one of both. Here, we remove TAX. 3. Reset up linear regression model considering the multicollinearity problem. PRICE = beta0 + beta1*SQFT + beta2*AGE + beta3*FEATS + beta4*NE + beta5*COR + err 4. Test the residuals for normality. SUMMARY OUTPUT Regression Statistics Multiple R 0.9 R Square 0.81 Adjusted R Square 0.79 Standard Error 183.95 Observations 66 ANOVA df SS MS Regression 5 8598477.12 1719695.42 Residual 60 2030286.63 33838.11 Total 65 10628763.76 Coefficients Standard Error t Stat Intercept 30.09 106.63 0.28 SQRT 0.69 0.05 13.79 AGE-3.6 1.91-1.88 FEATS 3.92 20.55 0.19 NE-6.54 53.61-0.12 COR-93.66 56.23-1.67 RESIDUAL OUTPUT Observation Predicted PRICE Residuals Standard Residuals 1 1824.55 225.45 1.28 2 1851.51 298.49 1.69 3 2042.77 107.23 0.61 4 1797.09 201.91 1.14 5 1797.09 102.91 0.58 6 1937.55-137.55-0.78 7 1358.45 201.55 1.14 8 1206.35 242.65 1.37 9 1290.63 84.37 0.48 10 1309.7-39.7-0.22 11 1458.2-208.2-1.18 12 1293.8-58.8-0.33 13 1314.53-144.53-0.82 14 1195.44-40.44-0.23 15 1007.35 102.65 0.58 16 1038.42 100.58 0.57 17 1015.63-20.63-0.12 18 841.84 58.16 0.33 19 1066.42-106.42-0.6 20 1854.24-159.24-0.9 21 1449.68 103.32 0.58 22 766.17 253.83 1.44 23 1014.3 5.7 0.03 24 697.34 152.66 0.86 25 643.72 76.28 0.43 26 1082.75-333.75-1.89 27 1989.03 160.97 0.91 28 1504.08-154.08-0.87 29 1743.74-444.74-2.52 30 1381.88-131.88-0.75 31 1160.73 78.27 0.44 32 1156.28-31.28-0.18 33 1456.88-376.88-2.13 34 1146.47-96.47-0.55 35 1218.09-169.09-0.96 36 1023.25-89.25-0.5 37 823.4 51.6 0.29 38 784.53 20.47 0.12 39 709.7 49.3 0.28 40 670.43 58.57 0.33 41 703.99 6.01 0.03 42 953.38 21.62 0.12 43 874.89 64.11 0.36 44 1405.39 694.61 3.93 45 705.9-125.9-0.71 46 1455.23 388.77 2.2 47 740.08-41.08-0.23 48 1209.28-49.28-0.28 49 1222.71222....
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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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Lab042122A - The data are a random sample of records of...

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