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Applied Linear Statistical Models:Applied Linear Regression Models(5版)

Applied

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訂購需時10-14天
9789863414179
Michael H. Kutner,Christopher J. Nachtsheim,John Neter,William Li
華泰文化
2019年9月04日
427.00  元
HK$ 405.65
省下 $21.35
 
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ISBN:9789863414179
  • 規格:平裝 / 740頁 / 18.6 x 23.2 x 3.5 cm / 普通級 / 單色印刷 / 5版
  • 出版地:台灣


  • [ 尚未分類 ]











      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.


     





    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




    其 他 著 作
    1. Applied Linear Statistical Models:Design and Analysis of Experiments(5版)