Question

Asked Nov 4, 2019

In comparing two multiple regression models, one with variables that are a subset of the other, bigger model's variables, to infer which model is superior, I get confused in looking at R^{2}, adjusted R^{2}, and F-statistic values. What's the difference among them and is one of these preferable to the others? Thanks.

Step 1

R-squared:

The coefficient of determination (*R ^{2}*) is defined as the proportion of variation in the observed values of the response variable that is explained by the regression. The squared correlation gives fraction of variability of response variable

The coefficient of determination, *R*-squared value is defined as,

Step 2

Where SSR is the sum of squares of regression, SST is the total sum of squares, SSE is the error sum of squares.

Adjusted R-squared:

The adjusted coefficient of determination (*R ^{2}_{Adj}*) is defined as the proportion of variation in the observed values of the response variable that is explained by the estimated regression model. If in the model the new terms arise the adjusted

The Adjusted R-squared value is defined as,

Step 3

*F*-statistics:

The general formula for the F...

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