Free Correlation and Regression MCQs with Answers

• A. 0
• B. 1
• C. 2
• D. Infinity

Explanation: The regression coefficients satisfy b_yx = r(s_y/s_x) and b_xy = r(s_x/s_y). Therefore, when r is zero and the standard deviations are nonzero, both regression coefficients are zero.

• A. Absolute measure
• B. Relative measure
• C. Both a and b
• D. None of the above

Explanation: A correlation coefficient is a relative, unit-free measure of the strength and direction of association. Its value remains unchanged if the measurement units are changed, unlike an absolute measure.

• A. Absolute measure
• B. Relative measure
• C. Both a and b
• D. None of these

Explanation: Regression coefficients carry units, such as units of the dependent variable per unit of the predictor, so regression is treated as an absolute measure in this classification. Correlation, by contrast, is unit-free and relative.

• A. Greater than 1
• B. Less than 1
• C. Remain same
• D. No effect

Explanation: The two regression coefficients satisfy Bxy × Byx = r², and r² cannot exceed 1. Therefore, if Bxy is greater than 1, Byx must be less than 1.

• A. Same
• B. Change
• C. Differ
• D. None of these

Explanation: Both regression coefficients have the same sign as the correlation coefficient because their signs are determined by r. Thus positive r gives positive regression coefficients, and negative r gives negative ones.

• A. 0to1
• B. -1to1
• C. 0to-1
• D. 0 to infinity

Explanation: A partial correlation coefficient measures the direction and strength of a linear relationship after controlling for other variables. Like ordinary correlation, it ranges from -1 to +1.

• A. Neyman person
• B. R A Fisher
• C. Sir F Galton
• D. Karl Pearson

Explanation: Sir Francis Galton introduced the term regression while studying the tendency of extreme parent and offspring measurements to move toward the average. Karl Pearson later developed important mathematical tools for regression and correlation.

• A. Regressand
• B. Predicted
• C. Response
• D. All of above

Explanation: The dependent variable is the response or predicted variable, and in regression terminology it is also called the regressand. Thus all three descriptions refer to the dependent variable.

• A. Fixed
• B. Random variable
• C. Whole
• D. None of above

Explanation: A response variable is the outcome being measured or predicted, so its observed value is treated as a random variable. The explanatory variable is used to explain or predict changes in the response.

• A. -1
• B. +1
• C. .6
• D. 0

Explanation: Independence implies zero covariance, and therefore the correlation coefficient is zero when the variances are defined and nonzero. Values such as +1 or -1 indicate perfect linear relationships, not independence.