Muhammad Imdad Ullah

The post is about MCQs correlation and regression. There are 20 multiple-choice questions covering topics related to the basics of correlation and regression analysis, best-fitting trends, least square regression lines, interpretation of correlation and regression coefficients, and regression plots. Let us start with the MCQs Correlation Regression Quiz.

Online MCQs on Correlation and Regression Analysis with Answers

1. If a straight line is fitted to data, then

 $ \sum Y > \sum \hat{Y} $

 $ \sum Y=\sum \hat{Y} $

 None of these

 $ \sum(Y-\hat{Y})^2=0 $

2. The correlation coefficient is the _________ of two regression coefficients.

 Median

 Geometric mean

 Arithmetic mean

 Harmonic mean

3. The process by which we estimate the value of dependent variable on the basis of one or more independent variables is called

 Association

 Slope Coefficients

 Regression

 Correlation

4. In the Least Square Regression Line, $\sum(Y-\hat{Y})^2$ is always

 Negative

 Fractional

 Non-Negative

 None

 Zero

5. The best-fitting trend is one for which the sum of squares of error is

 None

 Maximum

 Zero

 Minimum (Least)

6. In Regression Analysis, the regression line ($Y=\alpha+\beta X$) always intersect at the point

 $ (\alpha, \alpha) $

 $ (X, Y) $

 None

 $ (0, 0) $

 $ (\overline{X}, \overline{Y}) $

7. If all the values fall on the same straight line and the line has a positive slope then what will be the value of the Correlation coefficient $r$:

 $ r = +1 $

 $ r = -1 $

 $r \ge 0$

 Zero

 $ 0 \le r \le 1 $

8. For the Least Square trend $\hat{Y}=\alpha+\beta X$

 $ \sum Y=\sum \hat{Y} $

 None of these

 $ \sum \hat{Y}=0 $

 $ \sum Y >\sum \hat{Y} $

9. In the regression equation $Y=a+bX$, the $Y$ is called

 Dependent variable

 Continuous variable

 dependent variable

 Independent variable

10. All the data points falling along a straight line is called

 Linear relationship

 Dot plot

 Non-linear relationship

 Scatter diagram

11. In the Least Square Regression line, the quantity $\sum(Y-\hat{Y})$ is always

 Positive

 Fractional

 None

 Negative

 Zero

12. The dependent variable in a regression line is

 Random variable

 Stochastic variable

 Qualitative variable

 Non-random variable

13. A relationship where the flow of the data points is best represented by a curve is called

 Linear relationship

 Non-linear relationship

 Negative relationship

 Association

14. The method of least squares directs that select a regression line where the sum of the squares of the deviations of the points from the regression line is

 Negative

 Minimum

 Maximum

 Zero

15. In the regression equation $Y=a+bX$, the $X$ is called

 Independent variable

 Estimated variable

 Dependent variable

 Predictand variable

16. The independent variable is also called

 Regressand

 Estimated

 Predictand

 Regressor

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

 Intercept

 Akaike Information Criterion

 Slope

 Coefficient of determination

18. Which one is equal to explained variation divided by total variation?

 Coefficient of Determination

 Coefficient of Correlation

 Standard Error of Estimate

 Sum of squares due to regression

19. The regression line always passes through

 Origin

 Mean of $X$ and Median of $Y$

 Median Point of $X$ and $Y$

 Mean Point of $X$ and $Y$

 From Regression Ceofficients

20. In Regression Analysis $\sum\hat{Y}$ is equal to

 None

 0

 $ \sum{Y} $

 $ a $

 $ bX $

 Loading …

MCQs Correlation Regression Analysis

• In Regression Analysis $\sum\hat{Y}$ is equal to
• In the Least Square Regression Line, $\sum(Y-\hat{Y})^2$ is always
• Which one is equal to explained variation divided by total variation?
• The best-fitting trend is one for which the sum of squares of error is
• If a straight line is fitted to data, then
• In Regression Analysis, the regression line ($Y=\alpha+\beta X$) always intersect at the point
• In the Least Square Regression line, the quantity $\sum(Y-\hat{Y})$ is always
• If all the values fall on the same straight line and the line has a positive slope then what will be the value of the Correlation coefficient $r$:
• For the Least Square trend $\hat{Y}=\alpha+\beta X$
• The regression line always passes through
• The process by which we estimate the value of dependent variable on the basis of one or more independent variables is called
• The method of least squares directs that select a regression line where the sum of the squares of the deviations of the points from the regression line is
• A relationship where the flow of the data points is best represented by a curve is called
• All the data points falling along a straight line is called
• The predicted rate of response of the dependent variable to changes in the independent variable is called
• The independent variable is also called
• In the regression equation $Y=a+bX$, the $Y$ is called
• In the regression equation $Y=a+bX$, the $X$ is called
• The dependent variable in a regression line is
• The correlation coefficient is the ———– of two regression coefficients.

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The post is about Correlation Regression Analysis MCQs. There are 20 multiple-choice questions. The quiz covers topics related to the coefficient of correlation, regression analysis, simple linear regression equations, interpretation of correlation, and regression coefficients. Let us start with the Correlation Regression Analysis MCQs Quiz.

Please go to Correlation Regression Analysis MCQs 4 to view the test

Online Correlation Regression Analysis MCQs with Answers

• The value of the coefficient of correlation lies between
• If the scatter diagram is drawn the scatter points lie on a straight line, then it indicates
• In the model $Y= mX+ a\,\,\,$, $Y$ is also known as the:
• The regression equation is the line with a slope passing through
• 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.
• The sample coefficient of correlation
• If the equation of the regression line is $y = 5$, then what result will you take out from it?
• Which of the following relationships holds
• In regression equation $y=\alpha + \beta X + e$, both $X$ and $y$ variables are
• If $R^2$ is zero, that is no collinearity/ Multicollinearity, the variance inflation factor (VIF) will be
• The method of least squares finds the best fit line that ———- the error between observed & estimated points on the line
• The predicted rate of response of the dependent variable to changes in the independent variable is called
• The slope of the regression line of $Y$ on $X$ is also called
• In a simple regression, the number of unknown constants are
• In a simple regression equation, the number of variables are
• If $Y=2+0.6x$ then the value of the slope will be
• Which of the following can never be taken as the coefficient of correlation?
• When $\beta_{yx}$ is positive, then $\beta_{xy}$ will be
• If $Y=2+0.6X$ then the value of $Y$-intercept will be
• If $r=0.6$ and $\beta_{yx}=1.8$ then $\beta_{xy} = ?$

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The post is about MCQs on Correlation and Regression Analysis with Answers. There are 20 multiple-choice questions on correlation and regression analysis, interpretation of correlation and regression coefficients, the relationship between variables, and correlation and regression coefficients. Let us start with MCQs on Correlation and Regression.

Please go to Important MCQs on Correlation and Regression 3 to view the test

MCQs on Correlation and Regression with Answers

• The coefficient of Correlation values lies between
• If $r_{xy} = -0.84$ then $r_{yx}=?$
• In Correlation, both variables are always
• If two variables oppose each other then the correlation will be
• A perfect negative correlation is signified by
• The Coefficient of Correlation between $U=X$ and $V=-X$ is
• The Coefficient of Correlation between $X$ and $X$ is
• The Coefficient of Correlation $r$ is independent of
• If $X$ and $Y$ are independent of each other, the Coefficient of Correlation is
• If $b_{yx} <0$ and $b_{xy} =<0$, then $r$ is
• If $r=0.6, b_{yx}=1.2$ then $b_{xy}=?$
• When the regression line passes through the origin then
• Two regression lines are parallel to each other if their slope is
• When $b_{xy}$ is positive, then $b_{yx}$ will be
• If $\hat{Y}=a$ then $r_{xy}$?
• When two regression coefficients bear the same algebraic signs, then the correlation coefficient will be
• It is possible that two regression coefficients have
• The regression coefficient is independent of
• In the regression line $Y=a+bX$
• In the regression line $Y=a+bX$, the following is always true

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