Which assumption is NOT required for linear regression?
Practice Questions
Q1
Which assumption is NOT required for linear regression?
Linearity
Homoscedasticity
Independence of errors
Normality of predictors
Questions & Step-by-Step Solutions
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.
