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Experiments

Author: C. F. Jeff Wu
Publisher: John Wiley & Sons
ISBN: 1118211537
Size: 11.40 MB
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Praise for the First Edition: "If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." —Journal of the American Statistical Association Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries—and sheds further light on existing ones—on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: Expected mean squares and sample size determination One-way and two-way ANOVA with random effects Split-plot designs ANOVA treatment of factorial effects Response surface modeling for related factors Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. Experiments, Second Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

Handbook Of Design And Analysis Of Experiments

Author: Angela Dean
Publisher: CRC Press
ISBN: 146650434X
Size: 57.74 MB
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Handbook of Design and Analysis of Experiments provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook gives a unified treatment of a wide range of topics, covering the latest developments. This carefully edited collection of 25 chapters in seven sections synthesizes the state of the art in the theory and applications of designed experiments and their analyses. Written by leading researchers in the field, the chapters offer a balanced blend of methodology and applications. The first section presents a historical look at experimental design and the fundamental theory of parameter estimation in linear models. The second section deals with settings such as response surfaces and block designs in which the response is modeled by a linear model, the third section covers designs with multiple factors (both treatment and blocking factors), and the fourth section presents optimal designs for generalized linear models, other nonlinear models, and spatial models. The fifth section addresses issues involved in designing various computer experiments. The sixth section explores "cross-cutting" issues relevant to all experimental designs, including robustness and algorithms. The final section illustrates the application of experimental design in recently developed areas. This comprehensive handbook equips new researchers with a broad understanding of the field’s numerous techniques and applications. The book is also a valuable reference for more experienced research statisticians working in engineering and manufacturing, the basic sciences, and any discipline that depends on controlled experimental investigation.

Essentials Of Modern Business Statistics With Microsoft Office Excel Book Only

Author: David R. Anderson
Publisher: Cengage Learning
ISBN: 1337298352
Size: 52.41 MB
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Discover an accessible introduction to business statistics as ESSENTIALS OF MODERN BUSINESS STATISTICS, 7E balances a conceptual understanding of statistics with real-world applications of statistical methodology. The book integrates Microsoft Excel 2016, providing step-by-step instructions and screen captures to help readers master the latest Excel tools. Extremely reader-friendly, this edition includes numerous tools to maximize the user's success, including Self-Test Exercises, margin annotations, insightful Notes and Comments, and real-world Methods and Applications exercises. Eleven new Case Problems, as well as new Statistics in Practice applications and real data examples and exercises, give readers opportunities to put concepts into practice. Readers find everything needed to acquire key Excel 2016 skills and gain a strong understanding of business statistics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Causal Inference For Statistics Social And Biomedical Sciences

Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 1316094391
Size: 46.59 MB
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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Statistical Analysis Of Designed Experiments

Author: Ajit C. Tamhane
Publisher: John Wiley & Sons
ISBN: 0471750433
Size: 64.81 MB
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A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab(R) software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.

Probability And Statistics Volume I

Author: Reinhard Viertl
Publisher: EOLSS Publications
ISBN: 1848260520
Size: 60.88 MB
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Probability and Statistics theme is a component of Encyclopedia of Mathematical Sciences in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. The Theme with contributions from distinguished experts in the field, discusses Probability and Statistics. Probability is a standard mathematical concept to describe stochastic uncertainty. Probability and Statistics can be considered as the two sides of a coin. They consist of methods for modeling uncertainty and measuring real phenomena. Today many important political, health, and economic decisions are based on statistics. This theme is structured in five main topics: Probability and Statistics; Probability Theory; Stochastic Processes and Random Fields; Probabilistic Models and Methods; Foundations of Statistics, which are then expanded into multiple subtopics, each as a chapter. These three volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs

Univariate Discrete Distributions

Author: Norman L. Johnson
Publisher: John Wiley & Sons
ISBN: 0471715808
Size: 51.81 MB
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This Set Contains: Continuous Multivariate Distributions, Volume 1, Models andApplications, 2nd Edition by Samuel Kotz, N. Balakrishnan andNormal L. Johnson Continuous Univariate Distributions, Volume 1, 2nd Editionby Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 2, 2nd Editionby Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discrete Multivariate Distributions by Samuel Kotz, N.Balakrishnan and Normal L. Johnson Univariate Discrete Distributions, 3rd Edition by SamuelKotz, N. Balakrishnan and Normal L. Johnson Discover the latest advances in discrete distributionstheory The Third Edition of the critically acclaimedUnivariate Discrete Distributions provides a self-contained,systematic treatment of the theory, derivation, and application ofprobability distributions for count data. Generalized zeta-functionand q-series distributions have been added and are covered indetail. New families of distributions, including Lagrangian-typedistributions, are integrated into this thoroughly revised andupdated text. Additional applications of univariate discretedistributions are explored to demonstrate the flexibility of thispowerful method. A thorough survey of recent statistical literature drawsattention to many new distributions and results for the classicaldistributions. Approximately 450 new references along with severalnew sections are introduced to reflect the current literature andknowledge of discrete distributions. Beginning with mathematical, probability, and statisticalfundamentals, the authors provide clear coverage of the key topicsin the field, including: Families of discrete distributions Binomial distribution Poisson distribution Negative binomial distribution Hypergeometric distributions Logarithmic and Lagrangian distributions Mixture distributions Stopped-sum distributions Matching, occupancy, runs, and q-series distributions Parametric regression models and miscellanea Emphasis continues to be placed on the increasing relevance ofBayesian inference to discrete distribution, especially with regardto the binomial and Poisson distributions. New derivations ofdiscrete distributions via stochastic processes and random walksare introduced without unnecessarily complex discussions ofstochastic processes. Throughout the Third Edition, extensiveinformation has been added to reflect the new role ofcomputer-based applications. With its thorough coverage and balanced presentation of theoryand application, this is an excellent and essential reference forstatisticians and mathematicians.

Optimization Heuristics In Econometrics

Author: Peter Winker
Publisher: Wiley
ISBN: 9780471856313
Size: 18.48 MB
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Many problems in statistics and econometrics offer themselves naturally to the use of optimization heuristics. Standard methods applied to highly complex problems often produce approximate results, of unknown quality, based on heavy assumptions. Optimization heuristic methods provide powerful results to many complex problems, combined with relatively simple implementation. The techniques used in optimization heurisitics can be applied to problems encountered in econometrics, statistics and operations research. * Offers a self-contained introduction to optimization heuristics in econometrics and statistics * Features many examples of optimization heuristic methods applied to real problems * Includes detailed coverage of the threshold accepting heuristic methods applied to real problems * Provides suggestions for further reading Split into three parts, the book opens with a general introduction to optimization in statistics and econometrics, followed by detailed discussion of a relatively new and very powerful optimization heuristic, threshold accepting. The final part consists of many applications of the methods described earlier, encompassing experimental design, model selection, aggregation of time series, and censored quantile regression models. Those researching and working in econometrics, statistics and operations research are given the tools to apply optimization heuristic methods to real problems in their work. Postgraduate students of statistics and econometrics will find the book provides a good introduction to optimization heuristic methods.

Methods For Meta Analysis In Medical Research

Author: A. J. Sutton
Publisher: John Wiley & Sons Incorporated
ISBN:
Size: 40.83 MB
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With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field. Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book: * Provides a thorough criticism and an up-to-date survey of meta-analysis methods * Emphasises the practical approach, and illustrates the methods by numerous examples * Describes the use of Bayesian methods in meta-analysis * Includes discussion of appropriate software for each analysis * Includes numerous references to more advanced treatment of specialist topics * Refers to software code used in the examples available on the authors' Web site Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Bayesian Statistical Modelling

Author: Peter Congdon
Publisher: John Wiley & Sons Inc
ISBN: 9780471496007
Size: 24.82 MB
Format: PDF, Kindle
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Bayesian methods draw upon previous research findings and combine them with sample data to analyse problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modelling presents an accessible overview of modelling applications from a Bayesian perspective. * Provides an integrated presentation of theory, examples and computer algorithms * Examines model fitting in practice using Bayesian principles * Features a comprehensive range of methodologies and modelling techniques * Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods * Includes extensive applications to health and social sciences * Features a comprehensive collection of nearly 200 worked examples * Data examples and computer code in WinBUGS are available via ftp Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies. Researchers in applied statistics, medical science, public health and the social sciences will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a good reference source for both researchers and students.