5. Which of the following statements is true? A. The residual sum of squares can never be greater than the explained sum of squares. B.) The explained sum of squares can never be greater than the total sum of squares. C. The R-squared can never be greater than the correlation coefficient. D. The correlation coefficient can never be greater than the slope of the regression line.
5. Which of the following statements is true? A. The residual sum of squares can never be greater than the explained sum of squares. B.) The explained sum of squares can never be greater than the total sum of squares. C. The R-squared can never be greater than the correlation coefficient. D. The correlation coefficient can never be greater than the slope of the regression line.
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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Transcribed Image Text:**Question 5:**
"Which of the following statements is true?"
- **A.** The residual sum of squares can never be greater than the explained sum of squares.
- **B.** The explained sum of squares can never be greater than the total sum of squares.
- **C.** The R-squared can never be greater than the correlation coefficient.
- **D.** The correlation coefficient can never be greater than the slope of the regression line.
**Explanation:**
The correct answer is indicated as **B**, which is circled.
**Concepts Explained:**
- **Residual Sum of Squares (RSS):** This represents the part of the total variance that is not explained by the model. It can be greater or lesser than the explained sum of squares depending on the model fit.
- **Explained Sum of Squares (ESS):** This is the portion of the total variance in the response variable accounted for by the model. It cannot be greater than the total sum of squares because the total is made up of both the explained and residual sums of squares (TSS = ESS + RSS).
- **R-squared:** This is a statistical measure representing the proportion of variance for a dependent variable that's explained by an independent variable or variables in a regression model.
- **Correlation Coefficient:** This is a measure of the strength and direction of a linear relationship between two variables.
Each of these terms plays a crucial role in regression analysis, which attempts to model and analyze the relationships between variables.
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