nachtsheim

． 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.

． 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.

．The flow has been improved throughout, combining related chapters and smoothing transitions between concepts. Overall, the text has been reduced from 32 chapters to 30 with one entirely new chapter.

．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.

．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.

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

．Expanded discussion of model selection methods and criteria including Akaike Information Criterion and the Schwarz Bayesian Criterion.

．Extensively revised coverage of logistic regression, including polytomous nominal and ordinal logistic regression models.

PARTⅠ SIMPLE LINEAR REGRESSION
Ch.1 Linear Regression with One Predictor Variable
Ch.2 Inferences in Regression and Correlation Analysis
Ch.3 Diagnostic and Remedial Measures
Ch.4 Simultaneous Inferences and Other Topics in Regression Analysis
Ch.5 Matrix Approach to Simple Linear Regression Analysis

PARTⅡ MULTIPLE LINEAR REGRESSION
Ch.6 Multiple Regression I
Ch.7 Multiple Regression II
Ch.8 Regression Models for Quantitative and Qualitative Predictors
Ch.9 Building the Regression Model I: Model Selection and Validation
Ch.10 Building the Regression Model II: Diagnostics
Ch.11 Building the Regression Model III: Remedial Measures
Ch.12 Autocorrelation in Time Series Data

PARTⅢ NONLINEAR REGRESSION
Ch.13 Introduction to Nonlinear Regression and Neural Networks
Ch.14 Logistic Regression, Poisson Regression, and Generalized Linear Models

PARTⅣ DESIGN AND ANALYSIS OF SINGLE-FACTOR STUDIES
Ch.15 Introduction to the Design of Experimental and Observational Studies
Ch.16 Single Factor Studies
Ch.17 Analysis of Factor-Level Means
Ch.18 ANOVA Diagnostics and Remedial Measures

PARⅤ MULTI-FACTOR STUDIES
Ch.19 Two Factor Studies with Equal Sample Sizes
Ch.20 Two Factor Studies-One Case per Treatment
Ch.21 Randomized Complete Block Designs
Ch.22 Analysis of Covariance
Ch.23 Two Factor Studies with Unequal Sample Sizes
Ch.24 MultiFactor Studies
Ch.25 Random and Mixed Effects Models

PARTⅥ SPECIALIZED STUDY DESIGNS
Ch.26 Nested Designs, Subsampling, and Partially Nested Designs
Ch.27 Repeated Measures and Related Designs
Ch.28 Balanced Incomplete Block, Latin Square, and Related Designs
Ch.29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs
Ch.30 Response Surface Methodology

APPENDIX A Some Basic Results in Probability and Statistics
APPENDIX B Tables
APPENDIX C Data Sets
APPENDIX D Rules for Develping ANOVA Models and Tables for Balanced Designs
APPENDIX E Selected Bibliography