Applied Linear Statistical Models：Design and Analysis of Experiments（5版）

　　1. Applied Linear Statistical Models contains 14 chapters on the Applied Linear Regression Models, plus an additional 16 chapters on single and multifactor ANOVA and Experimental Design. The standard regression framework is emphasized throughout the text.

　　2. A new Chapter 15, Introduction to the Design of Experiments and Observational Studies provides a basic framework for the design and analysis of scientific studies.

　　3. Updated throughout to include the latest developments and methods in statistics such as advanced bootstrapping, neural networks, regression trees, other blocking approaches, Taguchi Methodology, and more.

　　4. New open ended 'Cases' based on data sets from business, health care, and engineering are included. Also, many problem data sets have been updated and expanded.

　　5. The text includes a CD with all data sets and the Student Solutions manual in PDF. In addition a new supplement, SAS and SPSS Program Solutions by Replogle and Johnson is available for the Fifth Edition.

作者簡介

Michael H. Kutner

　　現職：Emory University

Christopher J. Nachtsheim

　　現職：University of Minnesota

John Neter

　　現職：University of Georgia

William Li

　　現職：University of Minnesota

PART IV: DESIGN AND ANALYSIS OF SINGLE-FACTOR STUDIES
Ch15 Introduction to the Design of Experimental and Observational Studies
Ch16 Single Factor Studies
Ch17 Analysis of Factor Level Means
Ch18 ANOVA Diagnostics and Remedial Measures

PART V: MULTI-FACTOR STUDIES
Ch19 Two-Factor Studies with Equal Sample Sizes
Ch20 Two-Factor Studies-One Case per Treatment
Ch21 Randomized Complete Block Designs
Ch22 Analysis of Covariance
Ch23 Two-Factor Studies with Unequal Sample Sizes
Ch24 Multi-Factor Studies
Ch25 Random and Mixed Effects Models

PART VI: SPECIALIZED STUDY DESIGNS
Ch26 Nested Designs, Subsampling, and Partially Nested Designs
Ch27 Repeated Measures and Related Designs
Ch28 Balanced Incomplete Block, Latin Square, and Related Designs
Ch29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs
Ch30 Response Surface Methodology