Catalogue


Statistics for experimenters : an introduction to design, data analysis, and model building /
George E. P. Box, William G. Hunter, J. Stuart Hunter.
imprint
New York : Wiley, c1978.
description
xviii, 653 p. : ill. ; 24 cm.
ISBN
0471093157
format(s)
Book
Holdings
A Look Inside
Excerpts
Flap Copy
Applied Regression Analysis Second Edition Norman Draper and Harry Smith, Jr. The revised, expanded Second Edition of this popular book by two authorities with more than 50 years of combined experience in the field. Draper and Smith offer a comprehensive introduction to fundamentals, concepts and applications. This Second Edition treats basic multiple linear regression, the examination of residuals, the planning of large regression studies, the theory of nonlinear estimation, the "practical" significance of regression, and much more. With many new exercises, examples, and references, this book is unusually versatile_a useful reference for professionals in many fields. 1981 (0 471-02995-5) 709 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild Nonlinear Regression carefully blends the theory and practice of nonlinear regression and includes numerous computational examples and graphs throughout. Treats the broad aspects of model building and statistical inference, and serves as a thorough introduction to the topic for graduate students and as an accessible reference source for professionals. Most chapters are self-contained, and new and important material relating to the concept of curvature and its growing role in statistical inference is included. Ideal for those training to be statisticians, biometricians, and econometricians, as well as providing research scientists with up-to-date, readable access to their fields. 1989 (0 471-61760-1) 768 pp. Statistical Design for Research Leslie Kish The issues of data collection and selection, vital to statistics, yet often neglected, are described in-depth in Statistical Design For Research, as well as the ramifying effects of statistical design in the social and health sciences, education, and market research. Emphasizing research design development, this important work includes a discussion of key methods_experimental designs, survey sampling, and controlled investigations_plus ample tables, figures and problem sets. 1987 (0 471-08359-3) 296 pp.
Flap Copy
Applied Regression Analysis Second Edition Norman Draper and Harry Smith, Jr. The revised, expanded Second Edition of this popular book by two authorities with more than 50 years of combined experience in the field. Draper and Smith offer a comprehensive introduction to fundamentals, concepts and applications. This Second Edition treats basic multiple linear regression, the examination of residuals, the planning of large regression studies, the theory of nonlinear estimation, the "practical" significance of regression, and much more. With many new exercises, examples, and references, this book is unusually versatile-a useful reference for professionals in many fields. 1981 (0 471-02995-5) 709 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild Nonlinear Regression carefully blends the theory and practice of nonlinear regression and includes numerous computational examples and graphs throughout. Treats the broad aspects of model building and statistical inference, and serves as a thorough introduction to the topic for graduate students and as an accessible reference source for professionals. Most chapters are self-contained, and new and important material relating to the concept of curvature and its growing role in statistical inference is included. Ideal for those training to be statisticians, biometricians, and econometricians, as well as providing research scientists with up-to-date, readable access to their fields. 1989 (0 471-61760-1) 768 pp. Statistical Design for Research Leslie Kish The issues of data collection and selection, vital to statistics, yet often neglected, are described in-depth in Statistical Design For Research, as well as the ramifying effects of statistical design in the social and health sciences, education, and market research. Emphasizing research design development, this important work includes a discussion of key methods-experimental designs, survey sampling, and controlled investigations-plus ample tables, figures and problem sets. 1987 (0 471-08359-3) 296 pp.
Summaries
Main Description
Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ''statistical thinking'' by using examples drawn from actual case studies.
Long Description
This fresh approach to statistics focuses on applications in the physical, engineering, biological, and social sciences. From the beginning it source of ideas is the scientific method itself and the need of the investigator to make his research as effective as possible through proper choice and conduct of experiments and appropriate analysis of data. While the book is unusual in the attention given to the scientific philosophy, it requires only elementary mathematics.Material is presented with the non-statistician in mind. After a problem is stated, appropriate statistical methods of design and analysis are discussed. And frequently, examples are presented for which standard mathematical assumptions are wrong, thus forcing the reader's attention onto the essential precautions necessary in the conduct of the experiment to ensure valid conclusions.In addition to many worked examples in the text, there are frequent exercises (with answers) throughout the book. There are also questions at the end of each chapter (for purposes of preview and review) and numerous problems at the end of each part of the book.Box, Hunter, and Hunter-all statistical practitioners themselves-provide scientists and engineers with a lucid introduction to the basic statistical methods needed for research. In the last part of the book, advanced topics, selected for their special interest to scientific investigators, are introduced in readily understood language. These include time series analysis, response surface methods, regression analysis, study of error transmission, and mechanistic model-building.
Back Cover Copy
This fresh approach to statistics focuses on applications in the physical, engineering, biological, and social sciences. From the beginning it source of ideas is the scientific method itself and the need of the investigator to make his research as effective as possible through proper choice and conduct of experiments and appropriate analysis of data. While the book is unusual in the attention given to the scientific philosophy, it requires only elementary mathematics. Material is presented with the non-statistician in mind. After a problem is stated, appropriate statistical methods of design and analysis are discussed. And frequently, examples are presented for which standard mathematical assumptions are wrong, thus forcing the reader's attention onto the essential precautions necessary in the conduct of the experiment to ensure valid conclusions. In addition to many worked examples in the text, there are frequent exercises (with answers) throughout the book. There are also questions at the end of each chapter (for purposes of preview and review) and numerous problems at the end of each part of the book. Box, Hunter, and Hunter-all statistical practitioners themselves-provide scientists and engineers with a lucid introduction to the basic statistical methods needed for research. In the last part of the book, advanced topics, selected for their special interest to scientific investigators, are introduced in readily understood language. These include time series analysis, response surface methods, regression analysis, study of error transmission, and mechanistic model-building.
Table of Contents
Science and Statistics
Comparing Two Treatments
Use of External Reference Distribution to Compare Two Means
Random Sampling and the Declaration of Independence
Randomization and Blocking with Paired Comparisons
Significance Tests and Confidence Intervals for Means, Variances, Proportions and Frequences
Comparing More Than Two Treatments
Experiments to Compare k Treatment Means
Randomized Block and Two-Way Factorial Designs
Designs with More Than One Blocking Variable
Measuring the Effects of Variables
Empirical Modeling
Factorial Designs at Two Levels
More Applications of Factorial Designs
Fractional Factorial Designs at Two Levels
More Applications of Fractional Factorial Designs
Building Models and Using Them
Simple Modeling with Least Squares (Regression Analysis)
Response Surface Methods
Mechanistic Model Building
Study of Variation
Modeling Dependence: Times Series
Appendix Tables
Index
Table of Contents provided by Publisher. All Rights Reserved.

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