Linear Mixed Models A Practical Guide (3版)

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Highly recommended by JASA, Technometrics, and other leading statistical journals, the first two editions of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Third Edition continues to lead readers step-by-step through the process of fitting LMMs. The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. All examples have been updated, with a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included, and there is a new chapter on power analysis for mixed-effects models. Features:•Dedicates an entire chapter to the key theories underlying LMMs for clustered, longitudinal, and repeated measures data •Provides descriptions, explanations, and examples of software code necessary to fit LMMs in SAS, SPSS, R, Stata, and HLM •Contains detailed tables of estimates and results, allowing for easy comparisons across software procedures •Presents step-by-step analyses of real-world data sets that arise from a variety of research settings and study designs, including hypothesis testing, interpretation of results, and model diagnostics •Integrates software code in each chapter to compare the relative advantages and disadvantages of each package •Supplemented by a website with software code, datasets, additional documents, and updates Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.

A FIRST COURSE IN THE DESIGN OF EXPERIMENTS A LINEAR MODELS APPROACH

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Most texts on experimental design fall into one of two distinct categories. There are theoretical works with few applications and minimal discussion on design, and there are methods books with limited or no discussion of the underlying theory. Furthermore, most of these tend to either treat the analysis of each design separately with little attempt to unify procedures, or they will integrate the analysis for the designs into one general technique. A First Course in the Design of Experiments: A Linear Models Approach stands apart. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general theory for linear models. The authors begin with a general introduction then lead students through the theoretical results, the various design models, and the analytical concepts that will enable them to analyze virtually any design. Rife with examples and exercises, the text also encourages using computers to analyze data. The authors use the SAS software package throughout the book, but also demonstrate how any regression program can be used for analysis. With its balanced presentation of theory, methods, and applications and its highly readable style, A First Course in the Design of Experiments proves ideal as a text for a beginning graduate or upper-level undergraduate course in the design and analysis of experiments.

Foundations of Linear and Generalized Linear Models (1版)

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<內容簡介> A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. ●An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods ●An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems ●Numerous examples that use R software for all text data analyses ●More than 400 exercises for readers to practice and extend the theory, methods, and data analysis ●A supplementary website with datasets for the examples and exercises <章節目錄> 1 Introduction to Linear and Generalized Linear Models 2 Linear Models: Least Squares Theory 3 Normal Linear Models: Statistical Inference 4 Generalized Linear Models: Model Fitting and Inference 5 Models for Binary Data 6 Multinomial Response Models 7 Models for Count Data 8 Quasi-Likelihood Methods 9 Modeling Correlated Responses 10 Bayesian Linear and Generalized Linear Modeling 11 Extensions of Generalized Linear Models Appendix A Supplemental Data Analysis Exercises Appendix B Solution Outlines for Selected Exercises 商品描述(中文翻譯) 內容簡介 《線性與廣義線性模型基礎》是一本對統計建模中最重要的概念和結果提供寶貴概述的書籍。這本書由一位經驗豐富的作者撰寫，清晰而全面地介紹了線性統計模型的關鍵概念和結果。本書通過討論模型背後的理論、R軟體應用和精心設計的模型示例，提供了最常用的統計模型的廣泛而深入的概述，以闡明關鍵思想並促進實際模型建立。 本書首先介紹了線性模型的基礎知識，例如模型擬合如何將數據投影到模型向量子空間上，以及數據的正交分解如何提供有關解釋變量效應的信息。隨後，本書介紹了最流行的廣義線性模型，包括用於分類數據的二項和多項羅吉斯回歸，以及用於計數數據的泊松和負二項對數線性模型。 本書還包括以下內容： - 弱分佈假設下的拟似然方法介紹，例如廣義估計方程方法 - 線性混合模型和帶有隨機效應的廣義線性混合模型，用於處理聚類相關數據、貝葉斯建模以及處理高維問題等問題的擴展 - 使用R軟體進行所有文本數據分析的眾多示例 - 超過400個練習題，供讀者練習和擴展理論、方法和數據分析能力 - 附帶網站提供示例和練習的數據集 無需翻譯的部分已移除。