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

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書名：Applied Linear Statistical Models: Design and Analysis of Experiments 5/e 作者：KUTNER 出版社：McGraw-Hill 出版日期：2019/09/00 ISBN：9789863414186 內容簡介 　　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. 目錄 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

Introduction to Linear Models and Statistical Inference (1版)

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【簡介】 A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students' skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book's Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences. 【目錄】