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Linear Regression – Assumption – 4 (Homoscedasticity In Details?)
Homoscedasticity Table Of Contents: What Is Homoscedasticity ? Why Is Homoscedasticity Important? How to Identify Homoscedasticity? Examples Of Homoscedasticity . Consequences of Violating Homoscedasticity. How to Fix Heteroscedasticity? In Summary. (1) What Is Homoscedasticity? Homoscedasticity is an assumption in linear regression that the variance of the errors (residuals) is constant across all levels of the independent variables. In other words, the spread of residuals should be roughly the same for all predicted values of the dependent variable. (2) Why Is Homoscedasticity Important? Homoscedasticity is a key assumption in linear regression because: Accuracy of Predictions: When the variance of residuals is
