This is a teaching text for the advanced statistics undergraduate or the beginning graduate student of statistics. It is assumed that the user of the text has had at least a full year course in applied or mathematical statistics. The text is intended for a one semester introductory course in the theory of linear statistical models.

Linear Statistical Models

Science & Math

Myers, Raymond H.

Milton, J.Susan

Mathematics

McGraw-Hill/Primis Custom Publishing

This classic book provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. It assumes readers have a background in calculus, and offers a unique balance of theory and methodology. Chapter topics cover an introduction to statistics and data analysis, probability, random variables and probability distributions, mathematical expectation, some discrete probability distributions, some continuous probability distributions, functions of random variables, fundamental sampling distributions and data descriptions, one- and two-sample estimation problems, one- and two-sample tests of hypotheses, simple linear regression and correlation, multiple linear regression and certain nonlinear regression models, one factor experiments: general, factorial experiments (two or more factors), 2k factorial experiments and fractions, nonparametric statistics, and statistical quality control. For individuals trying to apply statistical concepts to real-life, and analyze and interpret data.

Probability and Statistics for Engineers and Scientists (7th Edition)

Ye, Keying

Walpole, Ronald E.

Myers, Sharon L.

Yee, Keying

PRENTICE/HALL

With its unique balance of theory and methodology, this classic text provides a rigorous introduction to basic probability theory and statistical inference, motivated by interesting, relevant applications. Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; ";dummy"; variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and l

Probability & Statistics for Engineers & Scientists

Pearson College Div

This classic book provides a rigorous introduction to basic probability theory and statistical inference that is well motivated by interesting, relevant applications. The new edition features many new, real-data based exercises and examples, an increased emphasis on the analysis of statistical output and greater use of graphical techniques and statistical methods in quality improvement.

Probability and Statistics for Engineers and Scientists

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

Probability and Statistics for Engineers and Scientists (9th Edition)

Ye, Keying E.

Pearson

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.   The StatCrunch eBook offers this text as a Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.

StatCrunch eText for Probability & Statistics for Engineers & Scientists (9th Edition)

Praise for the Second Edition: "This book [is for] anyone who would like a good, solid understanding of response surface methodology. The book is easy to read, easy to understand, and very applicable. The examples are excellent and facilitate learning of the concepts and methods." —Journal of Quality Technology Complete with updates that capture the important advances in the field of experimental design, Response Surface Methodology, Third Edition successfully provides a basic foundation for understanding and implementing response surface methodology (RSM) in modern applications. The book continues to outline the essential statistical experimental design fundamentals, regression modeling techniques, and elementary optimization methods that are needed to fit a response surface model from experimental data. With its wealth of new examples and use of the most up-to-date software packages, this book serves as a complete and modern introduction to RSM and its uses across scientific and industrial research. This new edition maintains its accessible approach to RSM, with coverage of classical and modern response surface designs. Numerous new developments in RSM are also treated in full, including optimal designs for RSM, robust design, methods for design evaluation, and experiments with restrictions on randomization as well as the expanded integration of these concepts into computer software. Additional features of the Third Edition include: <ul>
<li> Inclusion of split-plot designs in discussion of two-level factorial designs, two-level fractional factorial designs, steepest ascent, and second-order models </li> <li> A new section on the Hoke design for second-order response surfaces </li> <li> New material on experiments with computer models </li> <li> Updated optimization techniques useful in RSM, including multiple responses </li> <li> Thorough treatment of presented examples and experiments using JMP 7, Design-Expert Version 7, and SAS software packages </li> <li> Revised and new exercises at the end of each chapter </li> <li> An extensive references section, directing the reader to the most current RSM research </li> </ul>
Assuming only a fundamental background in statistical models and matrix algebra, Response Surface Methodology, Third Edition is an ideal book for statistics, engineering, and physical sciences courses at the upper-undergraduate and graduate levels. It is also a valuable reference for applied statisticians and practicing engineers.

Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Montgomery, Douglas C.

Anderson-Cook, Christine M.

Experiments, Instruments & Measurement

wiley

Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." ―Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: <ul>
<li> A new chapter on random effects and designs for GLMs </li> <li> A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion </li> <li> A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models </li> <li> Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights </li> <li> Illustrations of R code to perform GLM analysis </li> </ul>
The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Generalized Linear Models: with Applications in Engineering and the Sciences

Vining, G. Geoffrey

Robinson, Timothy J.

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.

Generalized Linear Models: With Applications in Engineering and the Sciences

Wiley-Interscience

Now updated and revised From the reviews of the First Edition . . . "Truly a book that can be read by practitioners...Anyone who deals with designing experiments, the statistical analysis and modeling of data, and especially product or process improvement, including optimization, should have this book as a reference." -Technometrics "An excellent book for practitioners. Ownership...is a professional necessity." -Journal of the American Statistical Association Identifying and fitting an appropriate response surface model from experimental data requires knowledge of statistical experimental design fundamentals, regression modeling techniques, and elementary optimization methods. This book integrates these three topics into a comprehensive, state-of-the-art presentation of response surface methodology (RSM). This new second edition has been substantially rewritten and updated to include new topics and material, new examples, and to more fully illustrate modern applications of RSM. The authors have made the computer a more integral part of their presentation, employing the most common and useful software packages. They bring an applied focus to the subject of RSM, emphasizing methods that are useful in industry for product and process design and development. Features include: * Coverage of two-level factorial and fractional factorial design, and empirical modeling of RSM * Optimization techniques useful in RSM, including multiple responses * Classical and modern response surface designs, including computer-generated designs * The RSM approach to robust parameter design and process robustness studies * Comprehensive treatment of mixture experiments * Revised and expanded end-of-chapter problems, an extensive reference section, and valuable technical appendices on RSM * Supported by Design-Expert software Response Surface Methodology develops the underlying theory of RSM, describes the assumptions and conditions necessary to successfully apply it, and provides comprehensive and authoritative discussion of current topics for statisticians, engineers, and students.

Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics)

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