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7) The data used above was split into 2 samples and the following regression results were obtained from the split data. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain. Be sure to write down your null and alternative hypothesis, calculate the test statistic, and find your critical value (use a 10% level of significance).

7) The data used above was split into 2 samples and the following regression results were obtained from the split data. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain. Be sure to write down your null and alternative hypothesis, calculate the test statistic, and find your critical value (use a 10% level of significance).

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter11: Data Analysis And Displays
Section11.4: Two-ways Tables
Problem 12E
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7) The data used above was split into 2 samples and the following regression results were obtained from the split data. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain. Be sure to write down your null and alternative hypothesis, calculate the test statistic, and find your critical value (use a 10% level of significance).
Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.950 0.902 0.818 48.5 Observations 14 ANOVA SS MS Significance F Regression 152083 25347 10.8 0.0031 Residual 16478 2354 Total 13 168561 Coefficients -31.0 Standard Error 1 Stat P-value Lower 95% -467.7 Upper 95% Intercept Land (acres) House Size(sq ft) Age 184.7 -0.2 0.9 405.6 278.9 90.0 3.1 0.0 66.2 491.6 0.089 0.035 2.537 0.039 0.006 0.171 -1.52 0.95 -3.76 -16.0 -1.61 0.15 0.71 Rooms 31.6 20.1 1.6 0.2 79.1 Baths 20.1 27.7 0.7 0.5 -45.4 85.6 Garage 13.5 33.3 0.4 0.7 -65.3 92.2 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.840 0.706 0.485 Standard Error 32.27 Observations 15 ANOVA MS Significance F df SS F 19998 3333 3.201 0.066 Regression Residual 8. 8331 1041 14 28330 Total Upper 95% 276.2 Standard Error t Stat P-value Lower 95% Соеficients 155.0 52.6 2.9 0.0 33.8 Intercept Land (acres) House Size(sq ft) 348.4 -291.8 -0.035 28.3 138.8 0.2 0.8 0.183 0.155 0.041 1.456 0.060 -0.291 0.591 -1.489 0.907 0.520 -0.560 Age 1.3 0.2 -6.3 23.8 8.8 6.5 Rooms
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.950 0.902 0.818 48.5 Observations 14 ANOVA SS MS Significance F Regression 152083 25347 10.8 0.0031 Residual 16478 2354 Total 13 168561 Coefficients -31.0 Standard Error 1 Stat P-value Lower 95% -467.7 Upper 95% Intercept Land (acres) House Size(sq ft) Age 184.7 -0.2 0.9 405.6 278.9 90.0 3.1 0.0 66.2 491.6 0.089 0.035 2.537 0.039 0.006 0.171 -1.52 0.95 -3.76 -16.0 -1.61 0.15 0.71 Rooms 31.6 20.1 1.6 0.2 79.1 Baths 20.1 27.7 0.7 0.5 -45.4 85.6 Garage 13.5 33.3 0.4 0.7 -65.3 92.2 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.840 0.706 0.485 Standard Error 32.27 Observations 15 ANOVA MS Significance F df SS F 19998 3333 3.201 0.066 Regression Residual 8. 8331 1041 14 28330 Total Upper 95% 276.2 Standard Error t Stat P-value Lower 95% Соеficients 155.0 52.6 2.9 0.0 33.8 Intercept Land (acres) House Size(sq ft) 348.4 -291.8 -0.035 28.3 138.8 0.2 0.8 0.183 0.155 0.041 1.456 0.060 -0.291 0.591 -1.489 0.907 0.520 -0.560 Age 1.3 0.2 -6.3 23.8 8.8 6.5 Rooms
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