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.
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30 of 31 people found the following review helpful:
5.0 out of 5 stars
A masterpiece,
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This review is from: Beyond ANOVA: Basics of Applied Statistics (Chapman & Hall/CRC Texts in Statistical Science) (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.
3 of 5 people found the following review helpful:
2.0 out of 5 stars
For statistics majors... maybe,
By Simulaman "Sim" (The dark side of the mind) - See all my reviews
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This review is from: Beyond ANOVA: Basics of Applied Statistics (Chapman & Hall/CRC Texts in Statistical Science) (Hardcover)
I am a Ph.D. student in operations research. This book was required for a graduate level statistics course. This book is difficult to understand. It is written more like a thesis than a text book. It is full of references like "For further discussion see Wilk and Gnanadesikan." In order to glean the subtleties from this text you must already be an expert on the material presented. If you are an expert in statistical analysis who seeks another perspective on how to analyze a set of data, then this might be your book. If you are a student, then this book will likely be useless. It was for me.
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Inside This Book
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First Sentence:
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 hypothesis about, the location of the population. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
monotone alternatives, probit plotting, single observation per cell, fixed effects case, studentized range distribution, probit plots, pivotal statistic, permutation distribution, unbalanced case, mixed effects model, normal theory, unequal variances, departures from assumptions, empirical weights, paired values, simultaneous confidence intervals, unbalanced designs, ratio estimators, nonparametric techniques, expected mean squares, trimmed mean, nonnormal distributions, paired data, robust tests Key Phrases - Capitalized Phrases (CAPs): (learn more)
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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 hypothesis about, the location of the population. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
monotone alternatives, probit plotting, single observation per cell, fixed effects case, studentized range distribution, probit plots, pivotal statistic, permutation distribution, unbalanced case, mixed effects model, normal theory, unequal variances, departures from assumptions, empirical weights, paired values, simultaneous confidence intervals, unbalanced designs, ratio estimators, nonparametric techniques, expected mean squares, trimmed mean, nonnormal distributions, paired data, robust tests Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 58
books:
See all 58 books this book cites
5
books
cite this book:
- Practical Longitudinal Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) by David J. Hand in Front Matter (1), and Front Matter (2)
- Computer-Aided Multivariate Analysis, Fourth Edition (Chapman & Hall/CRC Texts in Statistical Science) by Abdelmonem Afifi in Front Matter
- Modelling Binary Data, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) by David Collett in Front Matter
- Statistical Adjustment of Data (Dover Books on Mathematics) by W. Edwards Deming in Back Matter
- Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) by Andrew Gelman in Front Matter
See all 58 books this book cites
- Statistical Computing: An Introduction to Data Analysis using S-Plus by Michael J. Crawley on page 279, and Back Matter
- Statistics: An Introduction using R by Michael J. Crawley in Back Matter
- Applied Multivariate Data Analysis: Volume 1: Regression and Experimental Design (Springer Texts in Statistics) by J. D. Jobson on page 80
- Common Errors in Statistics: (and How to Avoid Them) by Phillip I. Good in Back Matter
- Common Errors in Statistics (and How to Avoid Them) by Phillip I. Good in Back Matter
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