Correlation and Regression MCQs

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Topic Notes: Correlation and Regression

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What is the implication when a linear regression line passes directly through the origin (0,0)?

In regression analysis, what is the process of verifying whether the underlying assumptions of the model are valid called?

Given the values ∑xy = 40, N = 100, ∑x² = 80, and ∑y² = 20, what is the calculated correlation coefficient?

Evaluate the following assertion and reason regarding linear relationships between variables: Assertion (A): A linear relationship between two variables does not necessarily imply an independent-dependent relationship. Reason (R): Causal relationships between variables may not always be supported by a sound theoretical framework.

In statistical terms, what degree of dispersion indicates that all data points lie exactly on a regression line?

Which one of the following formulae is used to calculate probable error of correlation coincident between two variables of 'n' pairs of observations?

A $$0.5758\left[ {\frac{{1 - {r^2}}}{n}} \right]$$ B $$0.675\left[ {\frac{{1 - {r^2}}}{n}} \right]$$ C $$0.6745\left[ {\frac{{1 - {r^2}}}{{\sqrt n }}} \right]$$ D $$0.5758\left[ {\frac{{1 - {r^2}}}{{\sqrt n }}} \right]$$

The intersecting point of two regression lines is

Given the regression equations x = 0.85y and y = 0.89x, what is the calculated value of the coefficient of correlation?

What does the linear equation Y = A + BX represent in statistical analysis?

Karl Pearson's correlation coefficient is calculated by which of the following formula?

A $$\frac{{{\text{Covariance of X and Y}}}}{{\sqrt {{\text{Var}}{\text{.}}\left( {\text{X}} \right)\,{\text{Var}}{\text{.}}\left( {\text{Y}} \right)} }}$$ B $$\frac{{{\text{Var}}{\text{.}}\left( {\text{X}} \right)\,{\text{Var}}{\text{.}}\left( {\text{Y}} \right)}}{{\sqrt {{\text{Covariance of X and Y}}} }}$$ C $$\frac{{{\text{Var}}{\text{.}}\left( {\text{X}} \right)\,{\text{Var}}{\text{.}}\left( {\text{Y}} \right)}}{{{\text{Covariance of X and Y}}}}$$ D $$\frac{{{\text{Covariance of X and Y}}}}{{{\text{Var}}{\text{.}}\left( {\text{X}} \right)\,{\text{Var}}{\text{.}}\left( {\text{Y}} \right)}}$$

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