06Life Expectancy80605009In certain settings, when we model Y as a function of X, the relationship does not appear tobe very linear.Sometimes, log transformations are applied to the variables to improve the linear fit of therelationship. This is especially common in settings in which one of the variables has extremeoutliers.The first figure below shows the results of a linear regression between life expectancy andaverage income (1000s). The second shows the results of a linear regression between lifeexpectancy and log average income. Which regression appears to better fit the data (i.e. inwhich figure does it appear that the observations more closely overlap with the linearregression line)?0690Regression of Life Expectancy on Average IncomeLife Expectancy700850506060°°500000°°00Average Income (1000s)o Life ExpectancyFitted values°100Regression of Life Expectancy on Log Average Income°°810Log Average Incomeo Life Expectancy.Fitted values°T12

The correct answer is second figure which is the log average income. The data seem to match the…

Answered: 06 Life Expectancy 80 60 50 09 In…

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter24: Forecasting Models

Section: Chapter Questions

Problem 6RP

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