Leading Personalities in Statistical Sciences

Leading Personalities in Statistical Sciences

Author: Norman L. Johnson

Publisher: John Wiley & Sons

Published: 2011-09-26

Total Pages: 432

ISBN-13: 1118150724

DOWNLOAD EBOOK

A fascinating chronicle of the lives and achievements of the menand women who helped shapethe science of statistics This handsomely illustrated volume will make enthralling readingfor scientists, mathematicians, and science history buffs alike.Spanning nearly four centuries, it chronicles the lives andachievements of more than 110 of the most prominent names intheoretical and applied statistics and probability. From Bernoullito Markov, Poisson to Wiener, you will find intimate profiles ofwomen and men whose work led to significant advances in the areasof statistical inference and theory, probability theory, governmentand economic statistics, medical and agricultural statistics, andscience and engineering. To help readers arrive at a fullerappreciation of the contributions these pioneers made, the authorsvividly re-create the times in which they lived while exploring themajor intellectual currents that shaped their thinking andpropelled their discoveries. Lavishly illustrated with more than 40 authentic photographs andwoodcuts * Includes a comprehensive timetable of statistics from theseventeenth century to the present * Features edited chapters written by 75 experts from around theglobe * Designed for easy reference, features a unique numbering schemethat matches the subject profiled with his or her particular fieldof interest


Introductory Statistics

Introductory Statistics

Author: William B. Ware

Publisher: Taylor & Francis

Published: 2013-02-15

Total Pages: 520

ISBN-13: 1136870113

DOWNLOAD EBOOK

This comprehensive and uniquely organized text is aimed at undergraduate and graduate level statistics courses in education, psychology, and other social sciences. A conceptual approach, built around common issues and problems rather than statistical techniques, allows students to understand the conceptual nature of statistical procedures and to focus more on cases and examples of analysis. Wherever possible, presentations contain explanations of the underlying reasons behind a technique. Importantly, this is one of the first statistics texts in the social sciences using R as the principal statistical package. Key features include the following. Conceptual Focus – The focus throughout is more on conceptual understanding and attainment of statistical literacy and thinking than on learning a set of tools and procedures. Problems and Cases – Chapters and sections open with examples of situations related to the forthcoming issues, and major sections ends with a case study. For example, after the section on describing relationships between variables, there is a worked case that demonstrates the analyses, presents computer output, and leads the student through an interpretation of that output. Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text. Companion Website – A companion website contains instructions on how to use R, SAS, and SPSS to solve the end-of-chapter exercises and offers additional exercises. Field Tested – The manuscript has been field tested for three years at two leading institutions.


Continuous Multivariate Distributions, Volume 1

Continuous Multivariate Distributions, Volume 1

Author: Samuel Kotz

Publisher: John Wiley & Sons

Published: 2004-04-05

Total Pages: 752

ISBN-13: 0471654035

DOWNLOAD EBOOK

Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications.


The Subjectivity of Scientists and the Bayesian Approach

The Subjectivity of Scientists and the Bayesian Approach

Author: S. James Press

Publisher: John Wiley & Sons

Published: 2012-01-20

Total Pages: 296

ISBN-13: 1118150627

DOWNLOAD EBOOK

Comparing and contrasting the reality of subjectivity in the workof history's great scientists and the modern Bayesian approach tostatistical analysis Scientists and researchers are taught to analyze their data from anobjective point of view, allowing the data to speak for themselvesrather than assigning them meaning based on expectations oropinions. But scientists have never behaved fully objectively.Throughout history, some of our greatest scientific minds haverelied on intuition, hunches, and personal beliefs to make sense ofempirical data-and these subjective influences have often aided inhumanity's greatest scientific achievements. The authors argue thatsubjectivity has not only played a significant role in theadvancement of science, but that science will advance more rapidlyif the modern methods of Bayesian statistical analysis replace someof the classical twentieth-century methods that have traditionallybeen taught. To accomplish this goal, the authors examine the lives and work ofhistory's great scientists and show that even the most successfulhave sometimes misrepresented findings or been influenced by theirown preconceived notions of religion, metaphysics, and the occult,or the personal beliefs of their mentors. Contrary to popularbelief, our greatest scientific thinkers approached their data witha combination of subjectivity and empiricism, and thus informallyachieved what is more formally accomplished by the modern Bayesianapproach to data analysis. Yet we are still taught that science is purely objective. Thisinnovative book dispels that myth using historical accounts andbiographical sketches of more than a dozen great scientists,including Aristotle, Galileo Galilei, Johannes Kepler, WilliamHarvey, Sir Isaac Newton, Antoine Levoisier, Alexander vonHumboldt, Michael Faraday, Charles Darwin, Louis Pasteur, GregorMendel, Sigmund Freud, Marie Curie, Robert Millikan, AlbertEinstein, Sir Cyril Burt, and Margaret Mead. Also included is adetailed treatment of the modern Bayesian approach to dataanalysis. Up-to-date references to the Bayesian theoretical andapplied literature, as well as reference lists of the primarysources of the principal works of all the scientists discussed,round out this comprehensive treatment of the subject. Readers will benefit from this cogent and enlightening view of thehistory of subjectivity in science and the authors' alternativevision of how the Bayesian approach should be used to further thecause of science and learning well into the twenty-first century.


Statistical Tests for Mixed Linear Models

Statistical Tests for Mixed Linear Models

Author: André I. Khuri

Publisher: John Wiley & Sons

Published: 2011-09-09

Total Pages: 384

ISBN-13: 1118164857

DOWNLOAD EBOOK

An advanced discussion of linear models with mixed or randomeffects. In recent years a breakthrough has occurred in our ability todraw inferences from exact and optimum tests of variance componentmodels, generating much research activity that relies on linearmodels with mixed and random effects. This volume covers the mostimportant research of the past decade as well as the latestdevelopments in hypothesis testing. It compiles all currentlyavailable results in the area of exact and optimum tests forvariance component models and offers the only comprehensivetreatment for these models at an advanced level. Statistical Tests for Mixed Linear Models: Combines analysis and testing in one self-containedvolume. Describes analysis of variance (ANOVA) procedures in balancedand unbalanced data situations. Examines methods for determining the effect of imbalance ondata analysis. Explains exact and optimum tests and methods for theirderivation. Summarizes test procedures for multivariate mixed and randommodels. Enables novice readers to skip the derivations and discussionson optimum tests. Offers plentiful examples and exercises, manyof which are numerical in flavor. Provides solutions to selected exercises. Statistical Tests for Mixed Linear Models is an accessiblereference for researchers in analysis of variance, experimentaldesign, variance component analysis, and linear mixed models. It isalso an important text for graduate students interested in mixedmodels.


Statistical Methods for Reliability Data

Statistical Methods for Reliability Data

Author: William Q. Meeker

Publisher: John Wiley & Sons

Published: 2014-08-21

Total Pages: 783

ISBN-13: 1118625978

DOWNLOAD EBOOK

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen. Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products. Statistical Methods for Reliability Data updates and improves established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. It includes methods for planning reliability studies and analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, general likelihood-based methods of handling arbitrarily censored data and truncated data, and more. In this book, engineers and statisticians in industry and academia will find: A wealth of information and procedures developed to give products a competitive edge Simple examples of data analysis computed with the S-PLUS system-for which a suite of functions and commands is available over the Internet End-of-chapter, real-data exercise sets Hundreds of computer graphics illustrating data, results of analyses, and technical concepts An essential resource for practitioners involved in product reliability and design decisions, Statistical Methods for Reliability Data is also an excellent textbook for on-the-job training courses, and for university courses on applied reliability data analysis at the graduate level. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon requestfrom the Wiley editorial department.