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c) A sample of 20 observations was divided into two equal sets after arranging for the independent variable, without omitting any central observations at all. The results for these two sets are given below. Test for the presence of heteroscedasticity using an appropriate test. Show all the steps involved and assumptions made. Regression results for set 1: y^ = 1.0536+0.8760 x Residual sum of squares: 3.8128 Regression results for set 2: y^ = 4.7110+0.7882 x Residual sum of squares: 25.7026 Critical value: 3.44

c) A sample of 20 observations was divided into two equal sets after arranging for the independent variable, without omitting any central observations at all. The results for these two sets are given below. Test for the presence of heteroscedasticity using an appropriate test. Show all the steps involved and assumptions made. Regression results for set 1: y^ = 1.0536+0.8760 x Residual sum of squares: 3.8128 Regression results for set 2: y^ = 4.7110+0.7882 x Residual sum of squares: 25.7026 Critical value: 3.44

ENGR.ECONOMIC ANALYSIS
ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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c) A sample of 20 observations was divided into two equal sets after arranging for the independent variable, without omitting any central observations at all. The results for these two sets are given below. Test for the presence of heteroscedasticity using an appropriate test. Show all the steps involved and assumptions made. Regression results for set 1: y^ = 1.0536+0.8760 x Residual sum of squares: 3.8128 Regression results for set 2: y^ = 4.7110+0.7882 x Residual sum of squares: 25.7026 Critical value: 3.44
Transcribed Image Text:c) A sample of 20 observations was divided into two equal sets after arranging for the independent variable, without omitting any central observations at all. The results for these two sets are given below. Test for the presence of heteroscedasticity using an appropriate test. Show all the steps involved and assumptions made. Regression results for set 1: y^ = 1.0536+0.8760 x Residual sum of squares: 3.8128 Regression results for set 2: y^ = 4.7110+0.7882 x Residual sum of squares: 25.7026 Critical value: 3.44
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