Lab041415A - Lab Activities Multiple Regression Analysis...

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Lab Activities: Multiple Regression Analysis Suppose that as a stat guru you're asked to analyze what kinds of individual's characteri To investigate this, you decided to run a regression of earnings on education level, job ex Using your statistical tools from E370 with the U.S. data from the National Longitudinal  ID : Respondent's identifier EARNINGS : Current hourly earnings ($) EDUC : Years of schooling (years) EXP : Total out-of-school work experience (years) MALE : Sex of respondent (1 if male, 0 if female) ID EARNINGS EDUC EXP MALE 1) Based on the resea 2302 30.44 17 17.58 1 73 57.69 18 11.17 1 2871 37.79 18 13.75 1 4022 18.49 12 21.58 1 941 27.00 13 21.83 1 3493 14.00 12 20.38 1 1186 38.81 16 13.02 1 997 22.18 12 19 1 2998 12.30 12 22.4 1 3) Use Excel to gener 1555 11.90 14 16.52 1 SUMMARY OUTPUT 1046 27.40 19 10.96 1 336 13.50 12 6.69 1 Regression St 11754 25.00 12 21.31 1 Multiple R 5375 10.00 12 20.04 1 R Square 482 81.82 19 16.88 1 Adjusted R Square 4245 15.38 14 15.04 1 Standard Error 12012 28.84 14 21.96 1 Observations 2463 9.61 15 20.75 1 3639 12.50 9 4.27 1 ANOVA 5390 15.93 14 23.27 1 1010 83.33 16 16.38 1 Regression 4900 18.75 12 21.12 1 Residual 2803 21.83 13 21.37 1 Total 1358 28.84 14 20.79 1 1911 16.71 12 22.08 1 5039 38.46 18 13.31 1 Intercept 1060 18.20 12 19.67 1 EDUC 5266 36.05 14 15.25 1 EXP 2904 25.76 19 11.71 1 MALE 3694 21.11 11 23.02 1 5203 23.60 13 14.19 1 4808 20.94 12 19.13 1 Independent variable Dependent variable: 2)  Before running re EARNINGS =  β 0  +  β 1 Expected  EARNINGS
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21.00 12 21.5 1 2189 17.94 16 16.04 1 4722 10.83 12 21.92 1 1708 15.69 16 16.92 1 493 18.00 13 17.19 1 2143 12.50 12 14.31 1 393 25.00 13 18.38 1 11872 11.39 14 10.75 1 2159 23.25 16 19.48 1 3202 14.25 12 21.67 1 2386 24.61 15 20.56 1 769 17.75 12 18.1 1 1081 15.00 14 18.29 1 The point estimate fo 1527 12.00 12 21.37 1 2443 8.00 14 1.92 1 by about $0.55 when 2725 8.00 14 18.15 1 2776 13.73 14 13.81 1 3979 48.07 19 10.33 1 6) Based upon the re 1397 12.44 12 23.4 1 who had worked for  2658 8.00 12 8.9 1 (EARNINGS)' = -31.8 5323 13.50 10 15.62 1 1368 11.92 16 18.33 1 1677 25.50 12 18.54 1 7) Explain why we ne 3399 27.77 12 20.83 1 As more regressors  3745 13.00 16 17.56 1 has no additional ex 1867 20.10 12 18.62 1 discount the increas 4314 9.80 12 17.35 1 570 22.43 16 17.37 1 8) Interpret the Adju 2733 10.56 12 22.38 1 4156 16.36 9 21.83 1 1318 12.30 8 22.44 1 4056 23.07 16 18.44 1 3933 13.73 12 19.54 1 4566 58.60 12 19.63 1 4239 12.00 12 21.65 1 12009 10.00 12 20.62 1 4119 9.85 9 20.48 1 860 12.00 12 20.62 1 3607 13.50 11 21.02 1 2372 56.00 16 12.04 1 3971 32.69 19 8.23 1 5222 14.24 16 15.29 1 2713 20.00 12 19.27 1 4191 39.31 18 14.13 1 4) Using both the crit H 0 β = 0 (in words,  H 1 β ≠ 0 (in words,   i) critical value meth ii)  p -value method: s Hence, we conclude 5) Interpret the inter
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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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Lab041415A - Lab Activities Multiple Regression Analysis...

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