Visualizing Hypothesis Tests in Multivariate Linear Models John Fox (McMaster University), Michael Friendly (York University), and Georges Monette (York University) Hypothesis-error (or "HE") plots, introduced by Friendly (2006, 2007), permit the visualization of hypothesis tests in multivariate linear models by representing hypothesis and error matrices of sums of squares and cross-products as ellipses. This paper describes the implementation of these methods in R, as well as their extension, for example from two to three dimensions and by scaling hypothesis ellipses and ellipsoids in a natural manner relative to error. The methods, incorporated in the heplots package for R, exploit new facilities in the car package for testing linear hypotheses in multivariate linear models and for constructing MANOVA tables for these models, including models for repeated measures.