# Correlation and Regression 4

This quiz is about MCQs Regression and Correlation analysis.

This Quiz contains MCQs about Correlation and 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, Autocorrelation, graphical representation of the relationship between the variables, etc. Let us start MCQs about Correlation and Regression Analysis.

1. Which of the following relationship holds

2. Which of the following can never be taken as coefficient of correlation?

3. The value of the coefficient of correlation lies between

4. The regression equation is the line with slope passing through

5. If the scatter diagram is drawn the scatter points lie on a straight line then it indicates

6. When $\beta_{yx}$ is positive, then $\beta_{xy}$ will be

7. If the regression equation is equal to $Y=23.6 – 54.2X$, then $23.6$ is the ______ while $-54.2$ is the ____ of the regression line.

8. If $R^2$ is zero, that is no collinearity/ Multicollinearity, the variance inflation factor (VIF) will be

9. In simple regression the number of unknown constants are

10. If $Y=2+0.6x$ then the value of slope will be

11. In the model $Y= mX+ a\,\,\,$, $Y$ is also known as the:

12. If $Y=2+0.6X$ then the value of $Y$-intercept will be

13. If the equation of the regression line is $y = 5$, then what result will you take out from it?

14. If $r=0.6$ and $\beta_{yx}=1.8$ then $\beta_{xy} = ?$

15. In simple regression equation, the number of variables are

16. The slope of the regression line of $Y$ on $X$ is also called

17. In regression equation $y=\alpha + \beta X + e$, both $X$ and $y$ variables are

19. The predicted rate of response of the dependent variable to changes in the independent variable is called

20. The method of least squares finds the best fit line that _______ the error between observed & estimated points on the line

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of the correlation lies between $-1$ and $1$. 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 graphical representation between the variables or numerical computation using 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.

Currently working as Assistant Professor of Statistics in Ghazi University, Dera Ghazi Khan. Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. l like Applied Statistics, Mathematics, and Statistical Computing. Statistical and Mathematical software used is SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis, etc.

### 6 Responses

1. GERTRUDE ABOAGYE says:

NICE QUESTIONS

2. Rose says:

3. sehrish malik says: