Applied Linear Statistical Models: Applied Linear Regression Models, 5/e (Paperback)
暫譯: 應用線性統計模型：應用線性回歸模型，第5版（平裝本）

Michael H. Kutner,Christopher J. Nachtsheim,John Neter,William Li

Applied Linear Statistical Models: Applied Linear Regression Models, 5/e (Paperback)

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商品描述

1. Added material on important techniques for data mining, including regression trees and neural network models in Chapters 11 and 13.
2. The Chapter on logistic regression (Chapter 14) has been extensively revised and expanded to include a more thorough treatment of logistic, probit, and complementary log-log models, logistic regression residuals, model selection, model assessment, logistic regression diagnostics, and goodness of fit tests. We have also developed new material on polytomous (multicategory) nominal logistic regression models and polytomous ordinal logistic regression models.
3. We have expanded the discussion of model selection methods and criteria. The Akaike information criterion and Schwarz Bayesian criterion have been added, and a greater emphasis is placed on the use of cross-validation for model selection and validation.
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.

商品描述(中文翻譯)

1. 在第11章和第13章中新增了有關資料探勘的重要技術的內容，包括迴歸樹和神經網路模型。
2. 邏輯迴歸章節（第14章）已進行了廣泛的修訂和擴充，包含對邏輯模型、Probit模型和補充對數模型的更深入探討，邏輯迴歸殘差、模型選擇、模型評估、邏輯迴歸診斷以及適合度檢驗。我們還開發了有關多類別（multicategory）名義邏輯迴歸模型和多類別序數邏輯迴歸模型的新材料。
3. 我們擴展了對模型選擇方法和標準的討論。新增了赤池資訊量準則（Akaike information criterion）和施瓦茨貝葉斯準則（Schwarz Bayesian criterion），並更強調使用交叉驗證進行模型選擇和驗證。
4. 新增了基於商業、醫療保健和工程數據集的開放式「案例」。此外，許多問題數據集已更新和擴充。
5. 本書附有一張CD，包含所有數據集和學生解答手冊的PDF版本。此外，還提供了Replogle和Johnson編寫的《SAS和SPSS程式解答》作為第五版的新補充材料。

目錄大綱

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

PART II: 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
Ch10 Building the Regression Model II: Diagnostics
Ch11 Building the Regression Model III: Remedial Measures
Ch12 Autocorrelation in Time Series Data

PART III: NONLINEAR REGRESSION
Ch13 Introduction to Nonlinear Regression and Neural Networks
Ch14 Logistic Regression, Poisson Regression, and Generalized Linear Models

目錄大綱(中文翻譯)

PART I: SIMPLE LINEAR REGRESSION

Ch 1 Linear Regression with One Predictor Variable

Ch 2 Inferences in Regression and Correlation Analysis

Ch 3 Diagnostics and Remedial Measures

Ch 4 Simultaneous Inferences and Other Topics in Regression Analysis

Ch 5 Matrix Approach to Simple Linear Regression Analysis



PART II: 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

Ch10 Building the Regression Model II: Diagnostics

Ch11 Building the Regression Model III: Remedial Measures

Ch12 Autocorrelation in Time Series Data



PART III: NONLINEAR REGRESSION

Ch13 Introduction to Nonlinear Regression and Neural Networks

Ch14 Logistic Regression, Poisson Regression, and Generalized Linear Models

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