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Design of Experiments Books
Algebraic and Geometric Methods in Statistics by
Publication Date: 2009-10-22
This up-to-date account of algebraic statistics and information geometry explores the emerging connections between the two disciplines, demonstrating how they can be used in design of experiments and how they benefit our understanding of statistical models, in particular, exponential models. Computer code and proofs are available online, where key examples are developed in further detail.
Analysis of Variance and Functional Measurement : A Practical Guide includes CD-ROM by
Publication Date: 2005-10-01
This book is a clear and straightforward guide to analysis of variance, the backbone of experimental research. It will show you how to interpret statistical results and translate them into prose that will clearly tell your audience what your data is saying. To help you become familiar with the techniques used in analysis of variance, there are plenty of end-of-chapter practice problems with suggested answers. The final chapter gives the first elementary presentation of functional measurement, or information integration theory, a methodology built upon analysis of variance that is a powerful technique for studyingcognitive processes. The accompanying CD contains CALSTAT, analysis of variance software that is easy to use.
ANOVA and ANCOVA: A GLM Approach by
Publication Date: 2011-10-25
With its organized and comprehensive presentation, the book successfully guides readers through conventional statistical concepts and how to interpret them in GLM terms, treating the main single- and multi-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of the separate development of ANOVA and regression analyses, and then goes on to demonstrate how both analyses are incorporated into the understanding of GLMs.
Contemporary Multivariate Analysis and Design of Experiments by
Publication Date: 2005-03-01
This book furthers new and exciting developments in experimental designs, multivariate analysis, biostatistics, model selection and related subjects. It features articles contributed by many prominent and active figures in their fields. These articles cover a wide array of important issues in modern statistical theory, methods and their applications. Distinctive features of the collections of articles are their coherence and advance in knowledge discoveries.
Design and Analysis of Experiments by
Publication Date: 2001-01-01
This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter.
Design and Analysis of Experiments in the Health Sciences by
Publication Date: 2012-07-24
This book clearly explains how to apply design and analysis principles in animal, human, and laboratory experiments while illustrating topics with applications and examples from randomized clinical trials and the modern topic of microarrays. The authors outline the following five types of designs that form the basis of most experimental structures: completely randomized designs; randomized block designs; factorial designs; multilevel experiments, repeated measures designs. A related website features a wealth of data sets that are used throughout the book, allowing readers to work hands-on with the material.
Design and Analysis of Experiments, Volume 3: Special Designs and Applications by
Publication Date: 2012-02-14
Design and Analysis of Experiments, Volume 3 continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. Topical coverage includes: genetic cross experiments, microarray experiments, and variety trials; clinical trials, group-sequential designs, and adaptive designs; fractional factorial and search, choice, and optimal designs for generalized linear models Computer experiments with applications to homeland security; robust parameter designs and split-plot type response surface designs; and analysis of directional data experiments. Related data sets and software applications are available on the book′s related FTP site.
Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models by
Publication Date: 2005-01-01
In addition to providing a protocol for testing a measurement system, the book presents an up-to-date summary of methods used to construct confidence intervals in normal-based random and mixed analysis of variance (ANOVA) models. Thus, this comprehensive book will be useful to scientists in all fields of application who wish to construct interval estimates for ANOVA model parameters. It includes approaches that can be applied to any ANOVA model.
Design of Experiments for Engineers and Scientists by
Publication Date: 2003-10-24
Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies.
Fundamental Concepts in the Design of Experiments by
Publication Date: 1999-03-25
Wide-ranging and accessible, this book shows students how to use applied statistics for planning, running, and analyzing experiments. Featuring over 350 problems taken from the authors' actual industrial consulting experiences, the text gives students valuable practice with real data and problem solving. The problems emphasize the basic philosophy of design and are simple enough for students with limited mathematical backgrounds to understand.
Introduction to Design of Experiments by
Publication Date: 2007-10-01
Illustrated with numerous real-world examples borrowed from a broad base of industries, this updated edition employs a gentle, progressive approach to using designed experiments. It covers basic ideas, terminology, and the application of techniques necessary to conduct a study using the design of experiments (DOE) framework.
Introduction to Engineering Statistics and Six SIGMA: Statistical Quality Control and Design of Experiments and Systems by
Publication Date: 2006-04-01
Introduction to Engineering Statistics and Six Sigma contains precise descriptions of all of the many related methods and details case studies showing how they have been applied in engineering and business to achieve millions of dollars of savings. Specifically, the methods introduced include many kinds of design of experiments (DOE) and statistical process control (SPC) charting approaches, failure mode and effects analysis (FMEA), formal optimization, genetic algorithms, gauge reproducibility and repeatability (R&R), linear regression, neural nets, simulation, quality function deployment (QFD) and Taguchi methods. A major goal of the book is to help the reader to determine exactly which methods to apply in which situation and to predict how and when the methods might not be effective. Illustrative examples are provided for all the methods presented and exercises based on case studies help the reader build associations between techniques and industrial problems. A glossary of acronyms provides familiarity with six sigma terminology and solutions to homework and practice exams are included.
Optimal Design of Experiments: A Case Study by
Publication Date: 2011-06-13
Examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.
Permutation, Parametric, and Bootstrap Tests of Hypotheses by
Publication Date: 2005-01-10
Previous edition sold over 1400 copies worldwide.This new edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.
Statistical Design and Analysis of Experiments by
Publication Date: 2003-02-14
Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results. Features numerous examples using actual engineering and scientific studies. Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions. Deep and concentrated experimental design coverage, with equivalent but separate emphasis on the analysis of data from the various designs. Topics can be implemented by practitioners and do not require a high level of training in statistics. New edition includes new and updated material and computer output.
World Class Quality: Using Design of Experiments to Make It Happen by
Publication Date: 2000-01-04
The book offers: a practical way to secure top management commitment and make "Design of Experiments" a way of life at any company; a new reliability technique that simulates field failures at the design stage so they can be prevented before production, a chapter summarizing related quality management and control techniques, making this an essential book for managers concerned with quality.
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