MCQ on Correlation and Regression

MCQ on Correlation and Regression: Statistical regression is a powerful data analysis technique that examines the relationship between a dependent variable and one or more independent variables. It helps model and predict outcomes, enabling researchers and analysts to understand how changes in independent variables affect the dependent variable.

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Regression analysis is used to:

In simple linear regression, the relationship between two variables is represented by:

The variable being predicted or explained in regression analysis is called the:

Which type of regression analysis is appropriate when there is one dependent variable and multiple independent variables?

The coefficient of determination (R-squared) in regression analysis represents:

If the correlation coefficient between two variables is -0.8, what can be said about the relationship between them in a linear regression?

Which of the following is a common method for estimating the parameters in regression analysis?

The residual in regression analysis is defined as:

A p-value in regression analysis measures:

In multiple linear regression, how can we determine the individual contribution of each independent variable to the dependent variable?

Heteroscedasticity in regression analysis refers to:

Which type of regression analysis is used when the dependent variable is categorical or binary?

Multicollinearity in multiple linear regression refers to:

When interpreting the intercept (constant) in regression analysis, it represents:

In regression analysis, which type of variable can be manipulated by the researcher to observe its effect on the dependent variable?

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