Asked by Vanessa Rodriguez on Jun 04, 2024
Verified
The value of the sum of squares for regression SSR can never be larger than the value of sum of squares for error SSE.
Sum Of Squares
A statistical measure that quantifies the dispersion or variation within a data set, typically used in variance and regression analysis.
Regression SSR
The sum of squares due to regression, indicating the explained variance by the regression model in the data.
Error SSE
The sum of squared errors in statistical analysis, indicating the discrepancy between observed and predicted values.
- Comprehend how the coefficient of determination, the coefficient of correlation, and the standard error of the estimate interrelate within regression analysis.
Verified Answer
ZK
Zybrea KnightJun 05, 2024
Final Answer :
False
Explanation :
The value of the sum of squares for regression (SSR) can indeed be larger than the value of the sum of squares for error (SSE), as SSR reflects the variation explained by the regression model, while SSE reflects the variation that is not explained by the model. The total variation (SST) is the sum of SSR and SSE, and there's no inherent constraint that SSR must be smaller than SSE.
Learning Objectives
- Comprehend how the coefficient of determination, the coefficient of correlation, and the standard error of the estimate interrelate within regression analysis.