Which assumption is NOT required for linear regression?

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Question: Which assumption is NOT required for linear regression?

Options:

Correct Answer: Normality of predictors

Solution:

Linear regression does not require the predictors to be normally distributed; it only requires the residuals to be normally distributed.

Which assumption is NOT required for linear regression?

Which assumption is NOT required for linear regression?

Step 1: Understand what linear regression is. It is a method used to model the relationship between a dependent variable and one or more independent variables (predictors).
Step 2: Know the assumptions of linear regression. Common assumptions include linearity, independence, homoscedasticity, and normality of residuals.
Step 3: Identify what 'residuals' are. Residuals are the differences between the observed values and the values predicted by the model.
Step 4: Recognize that for linear regression, the predictors (independent variables) do not need to be normally distributed. This means you can use any type of predictor data.
Step 5: Understand that the only requirement regarding normality is for the residuals. The residuals should be normally distributed for the model to make valid inferences.

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