Beyond Anova, Basics of Applied Statistics (Wiley Series in Probability and Statistics) (Hardcover)

~ Rupert G., Jr. Miller (Author) "The simplest problem is that of a sample from a single population where the aim of the statistical analysis is to estimate, or test a..." (more)
Key Phrases: monotone alternatives, probit plotting, single observation per cell, Monte Carlo (more...)

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Product Description

This book goes beyond specific methods in specific applications to consider the full range of techniques which can be used to solve a statistical problem. This applied text treats general topics such as one- and two-sample problems, one- and two-way classifications, regression analysis, ratios and variances. It includes helpful problem-solving hints and references along with theoretical and real-life exercises at the end of each chapter.

Product Details

• Hardcover: 334 pages
• Publisher: John Wiley & Sons Inc (March 1986)
• Language: English
• ISBN-10: 0471819220
• ISBN-13: 978-0471819226
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon.com Sales Rank: #2,323,153 in Books (See Bestsellers in Books)

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29 of 29 people found the following review helpful:

5.0 out of 5 stars A masterpiece, January 5, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

This review is from: Beyond ANOVA: Basics of Applied Statistics (Texts in Statistical Science Series) (Hardcover)

This text was original published in the early 1980s. It was the outgrowth of the lecture notes that Rupert produced and I ahd the priviledge of taking the two quarter course in Applied Statistics from Rupert Miller. Our course that Rupert taught so brilliantly to students before during and after my years at Stanford was originally published by Wiley and should have become one of the books in the Wiley Classic Series as his Survival Analysis book became. The book has been overlooked and underrated. Nevertheless I consider it to still be the best introductory graduate level text on linear models. It is especially noteworthy for its great organizational style and clarity. Miller gets to the heart of the methods and their applications and for each case he looks carefully at the modelling assumptions and what the effect of violations to normality and variance homogeneity have on the analysis. He also presents the alternative techniques to use when the results are heavily affected by assumption violations. I often go back to this book as a reference and I am glad that Chapman and Hall/CRC had the wisdom to reprint this text so that a new genration of statisticians can benefit from it and use it in their professional life.