Applied Linear Statistical Models: Applied Linear Regression Models (5 Ed. +CD) | 誠品線上

Applied Linear Statistical Models: Applied Linear Regression Models (5 Ed. +CD)

內容簡介

內容簡介 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.

作者介紹

作者介紹 Michael H. Kutner現職：Emory UniversityChristopher J. Nachtsheim現職：University of MinnesotaJohn Neter現職：University of GeorgiaWilliam Li現職：University of Minnesota

產品目錄

產品目錄 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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