BN.com Gift Guide

Overview

Praise for the Fourth Edition

"As with previous editions, the authors have produced a leading textbook on regression."
Journal of the American Statistical Association

A comprehensive and up-to-date introduction to the fundamentals of regression analysis

Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s ...

See more details below
Introduction to Linear Regression Analysis

Available on NOOK devices and apps  
  • NOOK Devices
  • Samsung Galaxy Tab 4 NOOK 7.0
  • Samsung Galaxy Tab 4 NOOK 10.1
  • NOOK HD Tablet
  • NOOK HD+ Tablet
  • NOOK eReaders
  • NOOK Color
  • NOOK Tablet
  • Tablet/Phone
  • NOOK for Windows 8 Tablet
  • NOOK for iOS
  • NOOK for Android
  • NOOK Kids for iPad
  • PC/Mac
  • NOOK for Windows 8
  • NOOK for PC
  • NOOK for Mac
  • NOOK for Web

Want a NOOK? Explore Now

NOOK Book (eBook)
$78.49
BN.com price
(Save 42%)$137.00 List Price
Note: This NOOK Book can be purchased in bulk. Please email us for more information.

Overview

Praise for the Fourth Edition

"As with previous editions, the authors have produced a leading textbook on regression."
Journal of the American Statistical Association

A comprehensive and up-to-date introduction to the fundamentals of regression analysis

Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.

Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:

  •  A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models
  • Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model
  • Tests on individual regression coefficients and subsets of coefficients
  • Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data.

In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material.

Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Read More Show Less

Editorial Reviews

SciTech Book
...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research.
SciTech Book
...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research.
Booknews
New edition of a text on regression analysis, a statistical technique for investigating and modeling the relationship between variables. Montgomery (industrial engineering, Arizona State U.), Elizabeth A. Peck (logistics modeling specialist, Coca-Cola Co.) and G. Geoffrey Vining (statistics, Virginia Tech) describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction, they outline a host of technical tools including basic inference procedures and introductory aspects of model adequacy checking, simple and multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, diagnostics for leverage and influence, polynomial regression models, indicator variables, variable selection and model building, multicollinearity, robust regression, generalized linear models, nonlinear regression, validation of regression models, and other topics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
“The book can be used for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. It also serves as a resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.” (Zentralblatt MATH, 1 October 2013)
Read More Show Less

Product Details

  • ISBN-13: 9781118627365
  • Publisher: Wiley
  • Publication date: 6/6/2013
  • Series: Wiley Series in Probability and Statistics
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 5
  • Pages: 672
  • Sales rank: 620,945
  • File size: 26 MB
  • Note: This product may take a few minutes to download.

Meet the Author

DOUGLAS C. MONTGOMERY, PhD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is a Fellow of the American Statistical Association, the American Society for Quality, the Royal Statistical Society, and the Institute of Industrial Engineers and has more than thirty years of academic and consulting experience. He has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. Dr. Montgomery is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition and Introduction to Time Series Analysis and Forecasting, both published by Wiley.

ELIZABETH A. PECK, PhD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.

G. GEOFFREY VINING, PhD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. He has published extensively in his areas of research interest, which include experimental design and analysis for quality improvement, response surface methodology, and statistical process control. A Fellow of the American Statistical Association and the American Society for Quality, Dr. Vining is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition (Wiley).

Read More Show Less

Table of Contents

PREFACE xiii

1. INTRODUCTION 1

1.1 Regression and Model Building 1

1.2 Data Collection 5

1.3 Uses of Regression 9

1.4 Role of the Computer 10

2. SIMPLE LINEAR REGRESSION 12

2.1 Simple Linear Regression Model 12

2.2 Least-Squares Estimation of the Parameters 13

2.3 Hypothesis Testing on the Slope and Intercept 22

2.4 Interval Estimation in Simple Linear Regression 29

2.5 Prediction of New Observations 33

2.6 Coeffi cient of Determination 35

2.7 A Service Industry Application of Regression 37

2.8 Using SAS and R for Simple Linear Regression 39

2.9 Some Considerations in the Use of Regression 42

2.10 Regression Through the Origin 45

2.11 Estimation by Maximum Likelihood 51

2.12 Case Where the Regressor x is Random 52

3. MULTIPLE LINEAR REGRESSION 67

3.1 Multiple Regression Models 67

3.2 Estimation of the Model Parameters 70

3.3 Hypothesis Testing in Multiple Linear Regression 84

3.4 Confidence Intervals in Multiple Regression 97

3.5 Prediction of New Observations 104

3.6 A Multiple Regression Model for the Patient Satisfaction Data 104

3.7 Using SAS and R for Basic Multiple Linear Regression 106

3.8 Hidden Extrapolation in Multiple Regression 107

3.9 Standardized Regression Coeffi cients 111

3.10 Multicollinearity 117

3.11 Why Do Regression Coeffi cients Have the Wrong Sign? 119

4. MODEL ADEQUACY CHECKING 129

4.1 Introduction 129

4.2 Residual Analysis 130

4.3 PRESS Statistic 151

4.4 Detection and Treatment of Outliers 152

4.5 Lack of Fit of the Regression Model 156

5. TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES 171

5.1 Introduction 171

5.2 Variance-Stabilizing Transformations 172

5.3 Transformations to Linearize the Model 176

5.4 Analytical Methods for Selecting a Transformation 182

5.5 Generalized and Weighted Least Squares 188

5.6 Regression Models with Random Effect 194

6. DIAGNOSTICS FOR LEVERAGE AND INFLUENCE 211

6.1 Importance of Detecting Infl uential Observations 211

6.2 Leverage 212

6.3 Measures of Infl uence: Cook’s D 215

6.4 Measures of Infl uence: DFFITS and DFBETAS 217

6.5 A Measure of Model Performance 219

6.6 Detecting Groups of Infl uential Observations 220

6.7 Treatment of Infl uential Observations 220

7. POLYNOMIAL REGRESSION MODELS 223

7.1 Introduction 223

7.2 Polynomial Models in One Variable 223

7.3 Nonparametric Regression 236

7.4 Polynomial Models in Two or More Variables 242

7.5 Orthogonal Polynomials 248

8. INDICATOR VARIABLES 260

8.1 General Concept of Indicator Variables 260

8.2 Comments on the Use of Indicator Variables 273

8.3 Regression Approach to Analysis of Variance 275

9. MULTICOLLINEARITY 285

9.1 Introduction 285

9.2 Sources of Multicollinearity 286

9.3 Effects of Multicollinearity 288

9.4 Multicollinearity Diagnostics 292

9.5 Methods for Dealing with Multicollinearity 303

9.6 Using SAS to Perform Ridge and Principal-Component Regression 321

10. VARIABLE SELECTION AND MODEL BUILDING 327

10.1 Introduction 327

10.2 Computational Techniques for Variable Selection 338

10.3 Strategy for Variable Selection and Model Building 351

10.4 Case Study: Gorman and Toman Asphalt Data Using SAS 354

11. VALIDATION OF REGRESSION MODELS 372

11.1 Introduction 372

11.2 Validation Techniques 373

11.3 Data from Planned Experiments 385

12. INTRODUCTION TO NONLINEAR REGRESSION 389

12.1 Linear and Nonlinear Regression Models 389

12.2 Origins of Nonlinear Models 391

12.3 Nonlinear Least Squares 395

12.4 Transformation to a Linear Model 397

12.5 Parameter Estimation in a Nonlinear System 400

12.6 Statistical Inference in Nonlinear Regression 409

12.7 Examples of Nonlinear Regression Models 411

12.8 Using SAS and R 412

13. GENERALIZED LINEAR MODELS 421

13.1 Introduction 421

13.2 Logistic Regression Models 422

13.3 Poisson Regression 444

13.4 The Generalized Linear Model 450

14. REGRESSION ANALYSIS OF TIME SERIES DATA 474

14.1 Introduction to Regression Models for Time Series Data 474

14.2 Detecting Autocorrelation: The Durbin-Watson Test 475

14.3 Estimating the Parameters in Time Series Regression Models 480

15. OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS 500

15.1 Robust Regression 500

15.2 Effect of Measurement Errors in the Regressors 511

15.3 Inverse Estimation—The Calibration Problem 513

15.4 Bootstrapping in Regression 517

15.5 Classifi cation and Regression Trees (CART) 524

15.6 Neural Networks 526

15.7 Designed Experiments for Regression 529

APPENDIX A. STATISTICAL TABLES 541

APPENDIX B. DATA SETS FOR EXERCISES 553

APPENDIX C. SUPPLEMENTAL TECHNICAL MATERIAL 574

C.1 Background on Basic Test Statistics 574

C.2 Background from the Theory of Linear Models 577

C.3 Important Results on SSR and SSRes 581

C.4 Gauss-Markov Theorem, Var(ε) = σ2I 587

C.5 Computational Aspects of Multiple Regression 589

C.6 Result on the Inverse of a Matrix 590

C.7 Development of the PRESS Statistic 591

C.8 Development of S2 (i) 593

C.9 Outlier Test Based on R-Student 594

C.10 Independence of Residuals and Fitted Values 596

C.11 Gauss–Markov Theorem, Var(ε) = V 597

C.12 Bias in MSRes When the Model Is Underspecifi ed 599

C.13 Computation of Infl uence Diagnostics 600

C.14 Generalized Linear Models 601

APPENDIX D. INTRODUCTION TO SAS 613

D.1 Basic Data Entry 614

D.2 Creating Permanent SAS Data Sets 618

D.3 Importing Data from an EXCEL File 619

D.4 Output Command 620

D.5 Log File 620

D.6 Adding Variables to an Existing SAS Data Set 622

APPENDIX E. INTRODUCTION TO R TO PERFORM LINEAR REGRESSION ANALYSIS 623

E.1 Basic Background on R 623

E.2 Basic Data Entry 624

E.3 Brief Comments on Other Functionality in R 626

E.4 R Commander 627

REFERENCES 628

INDEX 642

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)