Statistical Rules of Thumb

Wiley Series in Probability and Statistics

Book 699
Sold by John Wiley & Sons
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Praise for the First Edition:

"For a beginner [this book] is a treasure trove; for anexperienced person it can provide new ideas on how better to pursuethe subject of applied statistics."
Journal of Quality Technology

Sensibly organized for quick reference, Statistical Rules ofThumb, Second Edition compiles simple rules that arewidely applicable, robust, and elegant, and each captures keystatistical concepts. This unique guide to the use of statisticsfor designing, conducting, and analyzing research studiesillustrates real-world statistical applications through examplesfrom fields such as public health and environmental studies. Alongwith an insightful discussion of the reasoning behind everytechnique, this easy-to-use handbook also conveys the variouspossibilities statisticians must think of when designing andconducting a study or analyzing its data.

Each chapter presents clearly defined rules related toinference, covariation, experimental design, consultation, and datarepresentation, and each rule is organized and discussed under fivesuccinct headings: introduction; statement and illustration of therule; the derivation of the rule; a concluding discussion; andexploration of the concept's extensions. The author also introducesnew rules of thumb for topics such as sample size for ratioanalysis, absolute and relative risk, ANCOVA cautions, anddichotomization of continuous variables. Additional features of theSecond Edition include:

  • Additional rules on Bayesian topics
  • New chapters on observational studies and Evidence-BasedMedicine (EBM)

  • Additional emphasis on variation and causation

  • Updated material with new references, examples, and sources

A related Web site provides a rich learning environment andcontains additional rules, presentations by the author, and amessage board where readers can share their own strategies anddiscoveries. Statistical Rules of Thumb, SecondEdition is an ideal supplementary book for courses inexperimental design and survey research methods at theupper-undergraduate and graduate levels. It also serves as anindispensable reference for statisticians, researchers,consultants, and scientists who would like to develop anunderstanding of the statistical foundations of their researchefforts. A related website www.vanbelle.org provides additionalrules, author presentations and more.

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About the author

GERALD van BELLE, PhD, is Professor in the Department of Biostatistics and the Department of Environmental and Occupational Health Sciences at the University of Washington. He is the author or coauthor of more than 130 journal articles and several books, including Biostatistics: A Methodology for the Health Sciences, also published by Wiley. A recipient of the 2003 Wiley Author of the Year Award (Mathematics and Statistics Section), Dr. van Belle is a Fellow of both the American Statistical Association and the American Association for the Advancement of Science.
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Additional Information

Publisher
John Wiley & Sons
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Published on
Sep 20, 2011
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Pages
304
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ISBN
9781118210369
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Language
English
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Genres
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
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Content Protection
This content is DRM protected.
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Praise for the First Edition

"This book . . . is a significant addition to the literature onstatistical practice . . . should be of considerable interest tothose interested in these topics."—International Journal ofForecasting

Recent research has shown that monitoring techniques alone areinadequate for modern Statistical Process Control (SPC), and thereexists a need for these techniques to be augmented by methods thatindicate when occasional process adjustment is necessary.Statistical Control by Monitoring and Adjustment, Second Editionpresents the relationship among these concepts and elementary ideasfrom Engineering Process Control (EPC), demonstrating how thepowerful synergistic association between SPC and EPC can solvenumerous problems that are frequently encountered in processmonitoring and adjustment.

The book begins with a discussion of SPC as it was originallyconceived by Dr. Walter A. Shewhart and Dr. W. Edwards Deming.Subsequent chapters outline the basics of the new integration ofSPC and EPC, which is not available in other related books.Thorough coverage of time series analysis for forecasting, processdynamics, and non-stationary models is also provided, and thesesections have been carefully written so as to require only anelementary understanding of mathematics. Extensive graphicalexplanations and computational tables accompany the numerousexamples that are provided throughout each chapter, and a helpfulselection of problems and solutions further facilitatesunderstanding.

Statistical Control by Monitoring and Adjustment, Second Editionis an excellent book for courses on applied statistics andindustrial engineering at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for statisticiansand quality control practitioners working in industry.

From a preeminent authority—a modern and applied treatmentof multiway data analysis

This groundbreaking book is the first of its kind to presentmethods for analyzing multiway data by applying multiway componenttechniques. Multiway analysis is a specialized branch of the largerfield of multivariate statistics that extends the standard methodsfor two-way data, such as component analysis, factor analysis,cluster analysis, correspondence analysis, and multidimensionalscaling to multiway data. Applied Multiway Data Analysis presents aunique, thorough, and authoritative treatment of this relativelynew and emerging approach to data analysis that is applicableacross a range of fields, from the social and behavioral sciencesto agriculture, environmental sciences, and chemistry.

General introductions to multiway data types, methods, andestimation procedures are provided in addition to detailedexplanations and advice for readers who would like to learn moreabout applying multiway methods. Using carefully laid out examplesand engaging applications, the book begins with an introductorychapter that serves as a general overview of multiway analysis,including the types of problems it can address. Next, the processof setting up, carrying out, and evaluating multiway analyses isdiscussed along with commonly encountered issues, such aspreprocessing, missing data, model and dimensionality selection,postprocessing, and transformation, as well as robustness andstability issues.

Extensive examples are presented within a unified frameworkconsisting of a five-step structure: objectives; data descriptionand design; model and dimensionality selection; results and theirinterpretation; and validation. Procedures featured in the book areconducted using 3WayPack, which is software developed by theauthor, and analyses can also be carried out within the R andMATLAB systems. Several data sets and 3WayPack can be downloadedvia the book's related Web site.

The author presents the material in a clear, accessible stylewithout unnecessary or complex formalism, assuring a smoothtransition from well-known standard two-analysis to multiwayanalysis for readers from a wide range of backgrounds. Anunderstanding of linear algebra, statistics, and principalcomponent analyses and related techniques is assumed, though theauthor makes an effort to keep the presentation at a conceptual,rather than mathematical, level wherever possible. Applied MultiwayData Analysis is an excellent supplement for component analysis andstatistical multivariate analysis courses at theupper-undergraduate and beginning graduate levels. The book canalso serve as a primary reference for statisticians, data analysts,methodologists, applied mathematicians, and social scienceresearchers working in academia or industry.

Visit the Related Website: ahref="http://three-mode.leidenuniv.nl/"http://three-mode.leidenuniv.nl//a,to view data from the book.

Bayesian methods combine the evidence from the data at hand withprevious quantitative knowledge to analyse practical problems in awide range of areas. The calculations were previously complex, butit is now possible to routinely apply Bayesian methods due toadvances in computing technology and the use of new samplingmethods for estimating parameters. Such developments together withthe availability of freeware such as WINBUGS and R have facilitateda rapid growth in the use of Bayesian methods, allowing theirapplication in many scientific disciplines, including appliedstatistics, public health research, medical science, the socialsciences and economics.

Following the success of the first edition, this reworked andupdated book provides an accessible approach to Bayesian computingand analysis, with an emphasis on the principles of priorselection, identification and the interpretation of real datasets.

The second edition:

Provides an integrated presentation of theory, examples,applications and computer algorithms.Discusses the role of Markov Chain Monte Carlo methods incomputing and estimation.Includes a wide range of interdisciplinary applications, and alarge selection of worked examples from the health and socialsciences.Features a comprehensive range of methodologies and modellingtechniques, and examines model fitting in practice using Bayesianprinciples.Provides exercises designed to help reinforce thereader’s knowledge and a supplementary website containingdata sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchersin applied statistics, medical science, public health and thesocial sciences, who will benefit greatly from the examples andapplications featured. The book will also appeal to graduatestudents of applied statistics, data analysis and Bayesian methods,and will provide a great source of reference for both researchersand students.

Praise for the First Edition:

“It is a remarkable achievement to have carried out such arange of analysis on such a range of data sets. I found this bookcomprehensive and stimulating, and was thoroughly impressed withboth the depth and the range of the discussions it contains.”– ISI - Short Book Reviews

“This is an excellent introductory book on Bayesianmodelling techniques and data analysis” –Biometrics

“The book fills an important niche in the statisticalliterature and should be a very valuable resource for students andprofessionals who are utilizing Bayesian methods.” –Journal of Mathematical Psychology

A comprehensive overview of Monte Carlo simulation that exploresthe latest topics, techniques, and real-world applications

More and more of today’s numerical problems found inengineering and finance are solved through Monte Carlo methods. Theheightened popularity of these methods and their continuingdevelopment makes it important for researchers to have acomprehensive understanding of the Monte Carlo approach.Handbook of Monte Carlo Methods provides the theory,algorithms, and applications that helps provide a thoroughunderstanding of the emerging dynamics of this rapidly-growingfield.

The authors begin with a discussion of fundamentals such as howto generate random numbers on a computer. Subsequent chaptersdiscuss key Monte Carlo topics and methods, including:

Random variable and stochastic process generationMarkov chain Monte Carlo, featuring key algorithms such as theMetropolis-Hastings method, the Gibbs sampler, and hit-and-runDiscrete-event simulationTechniques for the statistical analysis of simulation dataincluding the delta method, steady-state estimation, and kerneldensity estimationVariance reduction, including importance sampling, latinhypercube sampling, and conditional Monte CarloEstimation of derivatives and sensitivity analysisAdvanced topics including cross-entropy, rare events, kerneldensity estimation, quasi Monte Carlo, particle systems, andrandomized optimization

The presented theoretical concepts are illustrated with workedexamples that use MATLAB®, a related Web sitehouses the MATLAB® code, allowing readers to workhands-on with the material and also features the author's ownlecture notes on Monte Carlo methods. Detailed appendices providebackground material on probability theory, stochastic processes,and mathematical statistics as well as the key optimizationconcepts and techniques that are relevant to Monte Carlosimulation.

Handbook of Monte Carlo Methods is an excellent referencefor applied statisticians and practitioners working in the fieldsof engineering and finance who use or would like to learn how touse Monte Carlo in their research. It is also a suitable supplementfor courses on Monte Carlo methods and computational statistics atthe upper-undergraduate and graduate levels.

This accessible new edition explores the major topics in MonteCarlo simulation

Simulation and the Monte Carlo Method, Second Editionreflects the latest developments in the field and presents a fullyupdated and comprehensive account of the major topics that haveemerged in Monte Carlo simulation since the publication of theclassic First Edition over twenty-five years ago. Whilemaintaining its accessible and intuitive approach, this revisededition features a wealth of up-to-date information thatfacilitates a deeper understanding of problem solving across a widearray of subject areas, such as engineering, statistics, computerscience, mathematics, and the physical and life sciences.

The book begins with a modernized introduction that addressesthe basic concepts of probability, Markov processes, and convexoptimization. Subsequent chapters discuss the dramatic changes thathave occurred in the field of the Monte Carlo method, with coverageof many modern topics including:

Markov Chain Monte CarloVariance reduction techniques such as the transform likelihoodratio method and the screening methodThe score function method for sensitivity analysisThe stochastic approximation method and the stochasticcounter-part method for Monte Carlo optimizationThe cross-entropy method to rare events estimation andcombinatorial optimizationApplication of Monte Carlo techniques for counting problems,with an emphasis on the parametric minimum cross-entropymethod

An extensive range of exercises is provided at the end of eachchapter, with more difficult sections and exercises markedaccordingly for advanced readers. A generous sampling of appliedexamples is positioned throughout the book, emphasizing variousareas of application, and a detailed appendix presents anintroduction to exponential families, a discussion of thecomputational complexity of stochastic programming problems, andsample MATLAB programs.

Requiring only a basic, introductory knowledge of probabilityand statistics, Simulation and the Monte Carlo Method,Second Edition is an excellent text for upper-undergraduate andbeginning graduate courses in simulation and Monte Carlotechniques. The book also serves as a valuable reference forprofessionals who would like to achieve a more formal understandingof the Monte Carlo method.

Praise for the First Edition

"This book . . . is a significant addition to the literature onstatistical practice . . . should be of considerable interest tothose interested in these topics."—International Journal ofForecasting

Recent research has shown that monitoring techniques alone areinadequate for modern Statistical Process Control (SPC), and thereexists a need for these techniques to be augmented by methods thatindicate when occasional process adjustment is necessary.Statistical Control by Monitoring and Adjustment, Second Editionpresents the relationship among these concepts and elementary ideasfrom Engineering Process Control (EPC), demonstrating how thepowerful synergistic association between SPC and EPC can solvenumerous problems that are frequently encountered in processmonitoring and adjustment.

The book begins with a discussion of SPC as it was originallyconceived by Dr. Walter A. Shewhart and Dr. W. Edwards Deming.Subsequent chapters outline the basics of the new integration ofSPC and EPC, which is not available in other related books.Thorough coverage of time series analysis for forecasting, processdynamics, and non-stationary models is also provided, and thesesections have been carefully written so as to require only anelementary understanding of mathematics. Extensive graphicalexplanations and computational tables accompany the numerousexamples that are provided throughout each chapter, and a helpfulselection of problems and solutions further facilitatesunderstanding.

Statistical Control by Monitoring and Adjustment, Second Editionis an excellent book for courses on applied statistics andindustrial engineering at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for statisticiansand quality control practitioners working in industry.

An accessible and practical approach to the design and analysis of experiments in the health sciences

Design and Analysis of Experiments in the Health Sciences provides a balanced presentation of design and analysis issues relating to data in the health sciences and emphasizes new research areas, the crucial topic of clinical trials, and state-of-the- art applications.

Advancing the idea that design drives analysis and analysis reveals the design, the 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 designsRandomized block designsFactorial designsMultilevel experimentsRepeated 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. In addition, an extensive bibliography outlines additional resources for further study of the presented topics.

Requiring only a basic background in statistics, Design and Analysis of Experiments in the Health Sciences is an excellent book for introductory courses on experimental design and analysis at the graduate level. The book also serves as a valuable resource for researchers in medicine, dentistry, nursing, epidemiology, statistical genetics, and public health.

Praise for the Second Edition

"A must-have book for anyone expecting to do research and/orapplications in categorical data analysis."
—Statistics in Medicine

"It is a total delight reading this book."
—Pharmaceutical Research

"If you do any analysis of categorical data, this is anessential desktop reference."
—Technometrics

The use of statistical methods for analyzing categorical datahas increased dramatically, particularly in the biomedical, socialsciences, and financial industries. Responding to new developments,this book offers a comprehensive treatment of the most importantmethods for categorical data analysis.

Categorical Data Analysis, Third Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial loglinear models for discrete datawith normal regression for continuous data. This edition alsofeatures:

An emphasis on logistic and probit regression methods forbinary, ordinal, and nominal responses for independent observationsand for clustered data with marginal models and random effectsmodelsTwo new chapters on alternative methods for binary responsedata, including smoothing and regularization methods,classification methods such as linear discriminant analysis andclassification trees, and cluster analysisNew sections introducing the Bayesian approach for methods inthat chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references torecent research and topics not covered in the text, linked to abibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for allexamples in the text, with information also about SPSS and Stataand with exercise solutions

Categorical Data Analysis, Third Edition is an invaluabletool for statisticians and methodologists, such as biostatisticiansand researchers in the social and behavioral sciences, medicine andpublic health, marketing, education, finance, biological andagricultural sciences, and industrial quality control.

From a preeminent authority—a modern and applied treatmentof multiway data analysis

This groundbreaking book is the first of its kind to presentmethods for analyzing multiway data by applying multiway componenttechniques. Multiway analysis is a specialized branch of the largerfield of multivariate statistics that extends the standard methodsfor two-way data, such as component analysis, factor analysis,cluster analysis, correspondence analysis, and multidimensionalscaling to multiway data. Applied Multiway Data Analysis presents aunique, thorough, and authoritative treatment of this relativelynew and emerging approach to data analysis that is applicableacross a range of fields, from the social and behavioral sciencesto agriculture, environmental sciences, and chemistry.

General introductions to multiway data types, methods, andestimation procedures are provided in addition to detailedexplanations and advice for readers who would like to learn moreabout applying multiway methods. Using carefully laid out examplesand engaging applications, the book begins with an introductorychapter that serves as a general overview of multiway analysis,including the types of problems it can address. Next, the processof setting up, carrying out, and evaluating multiway analyses isdiscussed along with commonly encountered issues, such aspreprocessing, missing data, model and dimensionality selection,postprocessing, and transformation, as well as robustness andstability issues.

Extensive examples are presented within a unified frameworkconsisting of a five-step structure: objectives; data descriptionand design; model and dimensionality selection; results and theirinterpretation; and validation. Procedures featured in the book areconducted using 3WayPack, which is software developed by theauthor, and analyses can also be carried out within the R andMATLAB systems. Several data sets and 3WayPack can be downloadedvia the book's related Web site.

The author presents the material in a clear, accessible stylewithout unnecessary or complex formalism, assuring a smoothtransition from well-known standard two-analysis to multiwayanalysis for readers from a wide range of backgrounds. Anunderstanding of linear algebra, statistics, and principalcomponent analyses and related techniques is assumed, though theauthor makes an effort to keep the presentation at a conceptual,rather than mathematical, level wherever possible. Applied MultiwayData Analysis is an excellent supplement for component analysis andstatistical multivariate analysis courses at theupper-undergraduate and beginning graduate levels. The book canalso serve as a primary reference for statisticians, data analysts,methodologists, applied mathematicians, and social scienceresearchers working in academia or industry.

Visit the Related Website: ahref="http://three-mode.leidenuniv.nl/"http://three-mode.leidenuniv.nl//a,to view data from the book.

Bayesian methods combine the evidence from the data at hand withprevious quantitative knowledge to analyse practical problems in awide range of areas. The calculations were previously complex, butit is now possible to routinely apply Bayesian methods due toadvances in computing technology and the use of new samplingmethods for estimating parameters. Such developments together withthe availability of freeware such as WINBUGS and R have facilitateda rapid growth in the use of Bayesian methods, allowing theirapplication in many scientific disciplines, including appliedstatistics, public health research, medical science, the socialsciences and economics.

Following the success of the first edition, this reworked andupdated book provides an accessible approach to Bayesian computingand analysis, with an emphasis on the principles of priorselection, identification and the interpretation of real datasets.

The second edition:

Provides an integrated presentation of theory, examples,applications and computer algorithms.Discusses the role of Markov Chain Monte Carlo methods incomputing and estimation.Includes a wide range of interdisciplinary applications, and alarge selection of worked examples from the health and socialsciences.Features a comprehensive range of methodologies and modellingtechniques, and examines model fitting in practice using Bayesianprinciples.Provides exercises designed to help reinforce thereader’s knowledge and a supplementary website containingdata sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchersin applied statistics, medical science, public health and thesocial sciences, who will benefit greatly from the examples andapplications featured. The book will also appeal to graduatestudents of applied statistics, data analysis and Bayesian methods,and will provide a great source of reference for both researchersand students.

Praise for the First Edition:

“It is a remarkable achievement to have carried out such arange of analysis on such a range of data sets. I found this bookcomprehensive and stimulating, and was thoroughly impressed withboth the depth and the range of the discussions it contains.”– ISI - Short Book Reviews

“This is an excellent introductory book on Bayesianmodelling techniques and data analysis” –Biometrics

“The book fills an important niche in the statisticalliterature and should be a very valuable resource for students andprofessionals who are utilizing Bayesian methods.” –Journal of Mathematical Psychology

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