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 modelBecause R Squared substantially underestimates the variability of the dependent variable accounted for by the independent variablesBecause 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.

Answered: Why is Adjusted R Squared preferred to…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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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.