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Introduction to Robust Estimation and Hypothesis Testing
Angielski
291 b
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Introduction to Robust Estimating and Hypothesis Testing, Fifth Edition, is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Coverage includes techniques for comparing groups and measuring effect size, current methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric techniques. The practical importance of these varied methods is illustrated using data from real world studies, and over 1700 R functions are included to support reader comprehension and practice. Includes the latest developments in robust regressionProvides many new and improved, accessible R functionsOffers comprehensive coverage of ANOVA and ANCOVA methods
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