Abstract
Rank-based procedures provide superior estimation and testing techniques when the data deviate from normality or contain gross outliers. However, these robust techniques are rarely incorporated in a nonparametric statistics or methods courses due to the lack of computational tools. One reason for this is the existence of certain unavoidable complexities in the numerical methods due to the absence of a closedform solution for the rank estimation problem. This article introduces a user interface, Web RGLM, which may be used to perform rank-based analyses of linear models across the World Wide Web. These models include simple location problems to complicated ANOVA and ANCOVA designs with multiple comparison procedures. The robust and least squares analyses are presented side-by-side for immediate comparisons. Web RGLM meets many of the computational demands of the classroom as well as the computational demands of quantitative researchers. Several illustrative examples are provided.
DOI
10.22237/jmasm/1209616020
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
