【簡介】 Praise for the Third Edition "...this is an excellent book which could easily be used as a course text..." —International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features: Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, and variance assumptions Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences. 《應用線性迴歸（第四版）》是對線性迴歸建模的基本理論和方法進行全面更新的書籍。本書展示了線性迴歸分析技術的實際應用，並提供了有趣的真實世界練習和例子。 新版強調了模型建立、參數理解、適配度和可靠性評估以及結論的重要概念，並通過最小二乘迴歸的使用來展示如何開發估計、信賴和測試程序。《應用線性迴歸（第四版）》保持了前幾版易於理解的特點，並新增了以下內容： - 在分析的初始探索階段、分析階段和總結階段強調了圖形方法。 - 深入探討了簡單和複雜模型中的參數估計、轉換和迴歸診斷。 - 新增了測試、ANOVA和變異假設等主題的材料。 - 更新了方法，如自助法、交叉驗證二項式和泊松迴歸以及現代模型選擇方法。 《應用線性迴歸（第四版）》是一本適合高年級本科生和研究生的優秀教科書，也是工程、工商管理、經濟學和社會科學等應用統計學家和實踐者的參考指南。 【目錄】 1 Scatterplots 2 Simple Linear Regression 3 Multiple Regression 4 Interpretation of Main Effects 5 Complex Regressors 6 Testing and Analysis of Variance 7 Variances 8 Transformations 9 Regression Diagnostics 10 Variable Selection 11 Nonlinear Regression 12 Binomial and Poisson Regression A.1 Website A.2 Means, Variances, Covariances and Correlations A.3 Least Squares for Simple Regression A.4 Means and Variances of Least Squares Estimates A.5 Estimating E(Y|X) using a Smoother A.6 A Brief Introduction to Matrices and Vectors A.7 Random Vectors A.8 Least Squares Using Matrices A.9 The QR factorization A.10 Spectral Decomposition A.11 Maximum Likelihood Estimates A.12 The Box–Cox Method for Transformations A.13 Case Deletion in Linear Regression