Sep 13, 2021 · This tutorial provides an explanation of the constant variance assumption in linear regression, including several examples.

In particular, since observations have different predictor values, this implies that the variance does not depend on any of the predictors or on the fitted values (which are linear combinations of predictor …

The constant variance assumption will be violated if the variance of the residuals: Changes by a factor of 2 or more between subgroups that differ in size by a factor of 2 or more.

It means that when you plot the individual error against the predicted value, the variance of the error predicted value should be constant. See the red arrows in the picture below, the length of the red …

There are various tests that may be performed on the residuals for testing if the regression errors have constant variance. It is usually sufficient to "visually" interpret residuals versus fitted values plots.

Dec 12, 2025 · The variance of residuals remains constant across all levels of the independent variables. Residuals should appear evenly scattered, indicating uniform error spread. Patterns of …

Constant variance, often referred to as homoscedasticity, is a fundamental concept in statistics and data analysis. It describes a situation where the variability of a dataset remains consistent across all levels …