Applied Linear Statistical Models: Applied Linear Regression Models, 5/e (Paperback)
Michael H. Kutner,Christopher J. Nachtsheim,John Neter,William Li
- 出版商: McGraw-Hill Education
- 出版日期: 2019-09-04
- 售價: $1,280
- 貴賓價: 9.8 折 $1,254
- 語言: 英文
- 頁數: 740
- ISBN: 9863414174
- ISBN-13: 9789863414179
-
相關分類:
Data Science、機率統計學 Probability-and-statistics、Machine Learning
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相關主題
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，新增了有關數據挖掘的重要技術的材料，包括回歸樹和神經網絡模型。
2. 對於邏輯回歸（第14章）進行了廣泛的修訂和擴充，包括更全面地介紹了邏輯回歸、概率回歸和互補對數-對數模型，邏輯回歸殘差，模型選擇，模型評估，邏輯回歸診斷和適配度檢驗。我們還新增了關於多類別名義邏輯回歸模型和多類別有序邏輯回歸模型的新材料。
3. 我們擴展了模型選擇方法和準則的討論。新增了阿卡貝克信息準則和施瓦茨貝葉斯準則，並更加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，許多問題數據集已經更新和擴展。
5. 本書附帶一張包含所有數據集和學生解答手冊的光碟，並且第五版還提供了一個新的補充資料，即由Replogle和Johnson編寫的SAS和SPSS程序解決方案。
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)
第一部分：簡單線性回歸
第1章：單一預測變數的線性回歸
第2章：回歸和相關分析的推論
第3章：診斷和修正措施
第4章：回歸分析中的同時推論和其他主題
第5章：簡單線性回歸分析的矩陣方法
第二部分：多元線性回歸
第6章：多元回歸I
第7章：多元回歸II
第8章：定量和定性預測變數的回歸模型
第9章：建立回歸模型I：模型選擇和驗證
第10章：建立回歸模型II：診斷
第11章：建立回歸模型III：修正措施
第12章：時間序列資料中的自相關
第三部分：非線性回歸
第13章：非線性回歸和神經網絡簡介
第14章：邏輯回歸、泊松回歸和廣義線性模型
