This set contains: 9780471794318 Common Errors in Statistics(and How to Avoid Them), Second Edition by Phillip I. Good, James W. Hardin and 9780471715757 Introduction to Statistics Through Resampling Methods and R/S-Plus by Phillip I. Good.

Common Errors in Statistics (and How to Avoid Them), Second Edition + Introduction to Statistics Through Resampling Methods and R/S-PLUS Set

Science & Math

Good, Phillip I.

Hardin, James W.

Mathematics

Wiley-Interscience

This set contains: 9780471794318 Common Errors in Statistics (and How to Avoid Them), 2nd Edition and 9780471715757 Introduction to Statistics Through Resampling Methods and R/S Plus?? both by Phillip I. Good and James W. Hardin.

Common Errors in Statistics (and How to Avoid Them), 2nd Edition + Introduction to Statistics Through Resampling Methods and Microsoft Office Excel

A guide to choosing and using the right techniques High-speed computers and prepackaged statistical routines would seem to take much of the guesswork out of statistical analysis and lend its applications readily accessible to all. Yet, as Phillip Good and James Hardin persuasively argue, statistical software no more makes one a statistician than a scalpel makes one a surgeon. Choosing the proper technique and understanding the analytical context is of paramount importance to the proper application of statistics. The highly readable Common Errors in Statistics (and How to Avoid Them) provides both newly minted academics and professionals who use statistics in their work with a handy field guide to statistical problems and solutions. Good and Hardin begin their handbook by establishing a mathematically rigorous but readily accessible foundation for statistical procedures. They focus on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. A handy checklist is provided to summarize the necessary steps. Topics covered include: * Creating a research plan * Formulating a hypothesis * Specifying sample size * Checking assumptions * Interpreting p-values and confidence intervals * Building a model * Data mining * Bayes' Theorem, the bootstrap, and many others Common Errors in Statistics (and How to Avoid Them) also contains reprints of classic articles from statistical literature to re-examine such bedrock subjects as linear regression, the analysis of variance, maximum likelihood, meta-analysis, and the bootstrap. With a final emphasis on finding solutions and on the great value of statistics when applied in the proper context, this book will prove eminently useful to students and professionals in the fields of research, industry, medicine, and government.

Common Errors in Statistics: (and How to Avoid Them)

Praise for the First Edition of Common Errors in Statistics " . . . let me recommend Common Errors to all those who interact with statistics, whatever their level of statistical understanding . . . " --Stats 40 " . . . written . . . for the people who define good practice rather than seek to emulate it." --Journal of Biopharmaceutical Statistics " . . . highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers." --The American Statistician " . . . I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good." --E-STREAMS A tried-and-true guide to the proper application of statistics Now in a second edition, the highly readable Common Errors in Statistics (and How to Avoid Them) lays a mathematically rigorous and readily accessible foundation for understanding statistical procedures, problems, and solutions. This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks. Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics. The Second Edition has been extensively revised to include: * Additional charts and graphs * Two new chapters, Interpreting Reports and Which Regression Method? * New sections on practical versus statistical significance and nonuniqueness in multivariate regression * Added material from the authors' online courses at statistics.com * New material on unbalanced designs, report interpretation, and alternative modeling methods With a final emphasis on both finding solutions and the great value of statistics when applied in the proper context, this book is eminently useful to students and professionals in the fields of research, industry, medicine, and government.

Common Errors in Statistics (and How to Avoid Them)

Octavo, Yellow Green Cloth Under Dust Jacket,1969, PP.167, Written By A Pediatrition And A Botanist For Plant Identification In Field, Lab And Clinic

Human poisoning from native and cultivated plants

Duke University Press

Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." ―CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." ―MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: <ul>
<li>Baseline data</li> <li>Detecting fraud</li> <li>Linear regression versus linear behavior</li> <li>Case control studies</li> <li>Minimum reporting requirements</li> <li>Non-random samples</li> </ul>
The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

wiley

Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book’s website. This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentation of hypothesis testing and diagnostics, covering competing hierarchical models; and a more detailed examination of previously discussed subjects. Along with doubling the number of end-of-chapter exercises, this edition expands discussion of various models associated with GEE, such as penalized GEE, cumulative and multinomial GEE, survey GEE, and quasi-least squares regression. It also offers a thoroughly new presentation of model selection procedures, including the introduction of an extension to the QIC measure that is applicable for choosing among working correlation structures. See Professor Hilbe discuss the book.

Generalized Estimating Equations

Hilbe, Joseph M.

Biological Sciences

Chapman and Hall/CRC

Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to accommodate the correlated data encountered in health research, social science, biology, and other related fields. Generalized Estimating Equations provides the first complete treatment of GEE methodology in all of its variations. After introducing the subject and reviewing GLM, the authors examine the different varieties of generalized estimating equations and compare them with other methods, such as fixed and random effects models. The treatment then moves to residual analysis and goodness of fit, demonstrating many of the graphical and statistical techniques applicable to GEE analysis. With its careful balance of origins, applications, relationships, and interpretation, this book offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice.

Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. Deftly balancing theory and application, the book stands out in its coverage of the derivation of the GLM families and their foremost links, while also guiding readers in the application of the various models to real data. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, generalized Poisson, and generalized binomial models. The book also includes a substantially expanded discussion of both proportional-odds and generalized ordered models, making it easy for readers to use these models in their own research.

Generalized Linear Models and Extensions, Second Edition

Stata Press

This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and random parameter models. Using Stata, the book offers numerous examples to assist you in applying the models to your own data situations.

Generalized Linear Models and Extensions, Third Edition

See the best price to sell, buy, or rent books by Hardin, James W.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Hardin, James W.

See the best price to sell, buy, or rent books by Hardin, James W.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Common Errors in Statistics (and How to Avoid Them), Second Edition + Introduction to Statistics Through Resampling Methods and R/S-PLUS Set

Common Errors in Statistics (and How to Avoid Them), 2nd Edition + Introduction to Statistics Through Resampling Methods and Microsoft Office Excel

Common Errors in Statistics: (and How to Avoid Them)

Common Errors in Statistics (and How to Avoid Them)

Human poisoning from native and cultivated plants

Common Errors in Statistics (and How to Avoid Them)

Generalized Estimating Equations

Generalized Estimating Equations

Generalized Linear Models and Extensions, Second Edition

Generalized Linear Models and Extensions, Third Edition