Why is Adjusted R Squared preferred to R Squared to assess the fit of a regression model? Because R Squared always increases when variables are added to the model Because R Squared substantially underestimates the variability of the dependent variable accounted for by the independent variables Because R Squared does not account for the possibility of the non-normal distribution of the dependent variable. Because R Squared measures a less relevant type of model fit than adjusted R Squared.
Why is Adjusted R Squared preferred to R Squared to assess the fit of a regression model? Because R Squared always increases when variables are added to the model Because R Squared substantially underestimates the variability of the dependent variable accounted for by the independent variables Because R Squared does not account for the possibility of the non-normal distribution of the dependent variable. Because R Squared measures a less relevant type of model fit than adjusted R Squared.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
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ISBN:9780079039897
Author:Carter
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Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 4GP
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Why is Adjusted R Squared preferred to R Squared to assess the fit of a regression model?
- Because R Squared always increases when variables are added to the model
- Because R Squared substantially underestimates the variability of the dependent variable accounted for by the independent variables
- Because R Squared does not account for the possibility of the non-
normal distribution of the dependent variable. - Because R Squared measures a less relevant type of model fit than adjusted R Squared.
Expert Solution
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Step 1
R squared is a measure in which the proportion of variance of the response variable that is explained by the explanatory variable. The adjusted R squared is an improvement over R squared that is adjusted for taking into account the number of predictors in the model.
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