# MCQs Correlation and Regregression 3

This quiz is about MCQs Regression and Correlation analysis.

This Section contains MCQs on Correlation Analysis, Simple Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions, Multicollinearity, Heteroscedasticity, and Autocorrelation, etc. Let us start MCQ on Correlation and Regression Analysis

MCQs about Correlation and Regression Analysis

1. A perfect negative correlation is signified by

2. The Coefficient of Correlation between $U=X$ and $V=-X$ is

3. Regression coefficient is independent of

4. In the regression line $Y=a+bX$ the following is always true

5. The Coefficient of Correlation between $X$ and $X$ is

6. If $r_{xy} = -0.84$ then $r_{yx}=?$

7. The Coefficient of Correlation $r$ is independent of

8. When $b_{xy}$ is positive, then $b_{yx}$ will be

9. When the regression line passes through the origin then

10. If $r=0.6, b_{yx}=1.2$ then $b_{xy}=?$

11. When two regression coefficients bears same algebraic signs, then correlation coefficient will be

12. If $X$ and $Y$ are independent of each other, the Coefficient of Correlation is

13. Coefficient of Correlation values lies between

14. If $\hat{Y}=a$ then $r_{xy}$?

15. In the regression line $Y=a+bX$

16. Two regression lines are parallel to each other if their slope is

17. It is possible that two regression coefficients have

18. If $b_{yx} <0$ and $b_{xy} =<0$, then $r$ is

19. In Correlation, both variables are always

20. If two variables oppose each other then the correlation will be

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The regression describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed either using a graphical representation between the variables or numerical computation using an appropriate computational formula.

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both of these can be used to indicate only how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.

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