In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

Residual plots can be used to validate assumptions about the regression model.

How Homoskedasticity Works Homoskedasticity is one assumption of linear regression modeling, and data of this type work well with the least squares method.

Halbert White, Glenn M. MacDonald, Some Large-Sample Tests for Nonnormality in the Linear Regression Model, Journal of the American Statistical Association, Vol. 75, No. 369 (Mar., 1980), pp. 16-28 ...

The linear regression model owes so much to Gauss that we believe it should bear his name. Other authors who made substantial contributions are: Cauchy who introduced the idea of orthogonality; ...

Description: An introduction to the modern techniques of econometrics and their applications. Topics include: the classical linear regression model (specification, estimation, inference, and ...