a. Explanation Given info: Correlation matrix of the urban and rural countries in the Unites states in mentioned. The study consists of six independent variables total county population ( x 1 ) , population density ( x 2 ) , population concentration ( x 3 ) , population growth ( x 4 ) , proportion of county land in farms ( x 5 ) , and 5-year change in agricultural land base ( x 6 ) and dependent variable is the urban/rural rating ( y ) of a county. Explanation: Condition for detecting multicollinearity using correlation coefficient : If | r | ≥ 0.8 there is extreme multicollinearity in the model. If 0.2 ≤ | r | ≤ 0.8 there is moderate multicollinearity in the model. If | r | < 0.2 there is low multicollinearity in the model. The correlation value between the variables x 1 and x 2 is 0.20, x 1 and x 3 is 0.45, x 2 and x 3 is 0.43, x 1 and x 4 is | − 0.05 | , x 4 and x 2 is | − 0.14 | , x 4 and x 3 is | − 0.01 | , x 1 and x 5 is | − 0 b. To determine whether there is evidence of extreme multicollinearity.

Statistics Plus New MyLab Statistics with Pearson eText -- Access Card Package (13th Edition)

13th Edition

ISBN: 9780134090436

Author: James T. McClave, Terry T Sincich

Publisher: PEARSON

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Chapter 12, Problem 12.154ACB

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The evidence of extreme multicollinearity using correlation matrix.

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whether there is evidence of extreme multicollinearity.

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Chapter 12, Problem 12.153ACB

Chapter 12, Problem 12.155ACB

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Statistics Plus New MyLab Statistics with Pearson eText -- Access Card Package (13th Edition)

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The evidence of extreme multicollinearity using correlation matrix.

Solution Summary: The author explains that there is no evidence of extreme multicollinearity in the model.

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The evidence of extreme multicollinearity using correlation matrix.

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Chapter 12 Solutions