Correlation and Regression MCQs

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Topic Notes: Correlation and Regression

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What term describes the statistical estimation of the relationship between a dependent variable and two or more independent variables?

What is the primary utility of a fitted least squares regression line?

A can only be computed if a strong linear relationship exists between x and y B is evidence for a cause-effect relationship between x and y C none of these alternatives is correct D may be used to predict a value of y if the corresponding x value is given

Calculate the correlation coefficient given: Covariance(X,Y) = -17.8, SD(X) = 6.6, SD(Y) = 4.2, and N = 20.

In a regression model, what is the specific name for the factor utilized to predict the dependent variable?

How is the covariance between two variables mathematically defined?

A None of the above B The average of the product of deviations taken from their averages C A is further divided by the product of their arithmetic averages D A is further divided by the product of their standard deviations

Assertion (A): Evidence of a linear relationship between two variables does not necessarily imply a cause-and-effect relationship. Reason (R): A statistical correlation does not guarantee a theoretical causal link.

A (A) is true, but (R) is false B Both (A) and (R) are true, but (R) is not the correct explanation of (A) C Both (A) and (R) are true and (R) is the correct explanation of (A) D (A) is false, but (R) is true

Which of the following formula is not correct? Select your answer from the options given below

A $${b_{yx}} = r\frac{{{\sigma _x}}}{{{\sigma _y}}}$$ B $$r = \sqrt {{b_{xy}} \times {b_{yx}}} $$ C $${b_{xy}} = r\frac{{{\sigma _x}}}{{{\sigma _y}}}$$ D $$y - \overline y = r\frac{{{\sigma _x}}}{{{\sigma _y}}}\left( {x - \overline x } \right)$$

Which of the following statements regarding correlation and regression analysis are considered true?

What is the correct formula for calculating the standard error of estimate?

A $$\sqrt {\frac{{\sum {{\left( {Y - {Y_c}} \right)}^2}}}{N}} $$ B $$\sqrt {\frac{{\sum {{\left( {Y - \overline Y } \right)}^2}}}{N}} $$ C $$\frac{{\sum {{\left( {Y - \overline Y } \right)}^2}}}{N}$$ D $$\frac{{\sum {{\left( {Y - {Y_c}} \right)}^2}}}{N}$$

In the linear regression equation X = a + bY, identify the role of variable X.

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Topic Notes: Correlation and Regression

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