Multiple Regression Model Assumptions

Easy Multiple Regression Analysis Model Assumptions

In a multiple regression analysis, certain assumptions must be satisfied for the model to provide valid results. Consider the following assumptions:

1. Linearity: The relationship between the independent and dependent variables is linear.
2. Homoscedasticity: The variance of the error terms is constant across all levels of the independent variable(s).
3. Independence: The residuals are independent from one another.

Which of the following statements about these assumptions is TRUE?

If the residuals display a pattern when plotted, it indicates the linearity assumption is violated.

Heteroscedasticity implies that the variance of the residuals is constant across observations.

Violation of the independence assumption suggests that residuals from one observation influence those of another.

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