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

Such model is called multiple linear regression or multivariable linear model. In essence, the additional predictors are expected to explain the variation in the response not explained by a simple ...

The author proposes some simple diagnostics for assessing the necessity of selected terms in smoothing spline ANOVA models. The elimination of practically insignificant terms generally enhances the ...

This article studies a new procedure to test for the equality of k regression curves in a fully non-parametric context. The test is based on the comparison of empirical estimators of the ...

In this workshop we look at where we have more than one predictor in our linear model. Start by downloading labC05.Rmd and load it into RStudio. Any text in italics reminds you that you need to have a ...