Written by engineers, it uses a practical, applied approach that is more oriented to engineering than any other text available. Instead of a few engineering examples mixed in with examples from other fields, all of its unique problem sets reflect the types of situations encountered by engineers in their working lives.

Product Description: This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It's packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives...read more

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Helps readers in learning how to design, conduct, and analyze experiments that optimize performance in products and processes. This book focuses on the use of the computer, providing output from two software products: Minitab and Design Expert. It also offers material on important topics, including follow-up experimentation and split-plot design.

Focusing on how statistical tools are integrated into the engineering problem-solving process, this book provides modern coverage of engineering statistics. It presents a wide range of techniques and methods that engineers will find useful in professional practice. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, building regression models, designing and analyzing engineering experiments, and more.

Product Description: Engineering Statistics, 3rd Edition, 2006 JustAsk! Edition

Product Description: This practical, user-oriented second edition describes how to use statistical modeling and analysis methods for forecasting and prediction problems. Statistical and mathematical terms are introduced only as they are needed, and every effort has been made to keep the mathematical and statistical prerequisites to a minimum...read more

Product Description: Includes thorough treatment of logistic and Poisson regression. * Introduction to generalized estimating questions. * Numerous examples in fields ranging from biology and biopharmaceuticals to engineering and quality assurance. * Provides guidance in using widely available software to illustrate all aspects of model-fitting, inference, and diagnostic testing...read more (view table of contents, read Amazon.com's description)

A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes 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 to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of: * Indicator variables, making the connection between regression and analysis-of-variance modelss * Variable selection and model-building techniques * The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures * Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation * Generalized linear models The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.

Product Description: This set includes the text Introduction to Linear Regression Analysis, 4th Edition by Dougla C. Montgomery, Elizabeth A. Peck and G. Geoffrey Vining and the Introduction to Linear Regression Analysis, Student Solutions Manual , 4th Edition.

Product Description: A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables...read more

(view table of contents)