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Common Errors In Statistics And How To Avoid Them

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 1118360117
Size: 59.41 MB
Format: PDF
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Praise for Common Errors in Statistics (and How to AvoidThem) "A very engaging and valuable book for all who use statistics inany setting." —CHOICE "Addresses popular mistakes often made in data collection andprovides an indispensable guide to accurate statistical analysisand reporting. The authors' emphasis on careful practice, combinedwith a focus on the development of solutions, reveals the truevalue of statistics when applied correctly in any area ofresearch." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), FourthEdition provides a mathematically rigorous, yet readilyaccessible foundation in statistics for experienced readers as wellas students learning to design and complete experiments, surveys,and clinical trials. Providing a consistent level of coherency throughout, the highlyreadable Fourth Edition focuses on debunking popular myths,analyzing common mistakes, and instructing readers on how to choosethe appropriate statistical technique to address their specifictask. The authors begin with an introduction to the main sources oferror and provide techniques for avoiding them. Subsequent chaptersoutline key methods and practices for accurate analysis, reporting,and model building. The Fourth Edition features newly addedtopics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, andan extensive bibliography with several hundred citations directingreaders to resources for further study. Presented in an easy-to-follow style, Common Errors inStatistics, Fourth Edition is an excellent book for studentsand professionals in industry, government, medicine, and the socialsciences.

Beyond Basic Statistics

Author: Kristin H. Jarman
Publisher: John Wiley & Sons
ISBN: 1118856120
Size: 29.70 MB
Format: PDF, Kindle
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Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.

Statistical Methods For Quality Improvement

Author: Thomas P. Ryan
Publisher: John Wiley & Sons
ISBN: 9781118058107
Size: 38.19 MB
Format: PDF
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Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." —Technometrics This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods. In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include: Updated coverage of control charts, with newly added tools The latest research on the monitoring of linear profiles and other types of profiles Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures New discussions on design of experiments that include conditional effects and fraction of design space plots New material on Lean Six Sigma and Six Sigma programs and training Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic. Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

Keeping Up With The Quants

Author: Thomas H. Davenport
Publisher: Harvard Business Press
ISBN: 1422187268
Size: 78.85 MB
Format: PDF
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Why Everyone Needs Analytical Skills Welcome to the age of data. No matter your interests (sports, movies, politics), your industry (finance, marketing, technology, manufacturing), or the type of organization you work for (big company, nonprofit, small start-up)—your world is awash with data. As a successful manager today, you must be able to make sense of all this information. You need to be conversant with analytical terminology and methods and able to work with quantitative information. This book promises to become your “quantitative literacy" guide—helping you develop the analytical skills you need right now in order to summarize data, find the meaning in it, and extract its value. In Keeping Up with the Quants, authors, professors, and analytics experts Thomas Davenport and Jinho Kim offer practical tools to improve your understanding of data analytics and enhance your thinking and decision making. You’ll gain crucial skills, including: • How to formulate a hypothesis • How to gather and analyze relevant data • How to interpret and communicate analytical results • How to develop habits of quantitative thinking • How to deal effectively with the “quants” in your organization Big data and the analytics based on it promise to change virtually every industry and business function over the next decade. If you don’t have a business degree or if you aren’t comfortable with statistics and quantitative methods, this book is for you. Keeping Up with the Quants will give you the skills you need to master this new challenge—and gain a significant competitive edge.

Introduction To Statistics Through Resampling Methods And R

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 1118497570
Size: 14.88 MB
Format: PDF, Kindle
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A highly accessible alternative approach to basic statistics Praisefor the First Edition: "Certainly one of the most impressivelittle paperback 200-page introductory statistics books that I willever see . . . it would make a good nightstand book for everystatistician."—Technometrics Written in a highly accessible style, Introduction to Statisticsthrough Resampling Methods and R, Second Edition guides students inthe understanding of descriptive statistics, estimation, hypothesistesting, and model building. The book emphasizes the discoverymethod, enabling readers to ascertain solutions on their own ratherthan simply copy answers or apply a formula by rote. TheSecond Edition utilizes the R programming language to simplifytedious computations, illustrate new concepts, and assist readersin completing exercises. The text facilitates quick learningthrough the use of: More than 250 exercises—with selected "hints"—scatteredthroughout to stimulate readers' thinking and to actively engagethem in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the finalchapter to assist readers in applying statistics to real-lifeapplications Introduction to Statistics through Resampling Methods and R, SecondEdition is an excellent resource for students and practitioners inthe fields of agriculture, astrophysics, bacteriology, biology,botany, business, climatology, clinical trials, economics,education, epidemiology, genetics, geology, growth processes,hospital administration, law, manufacturing, marketing, medicine,mycology, physics, political science, psychology, social welfare,sports, and toxicology who want to master and learn to applystatistical methods.

Crc Handbook Of Sample Size Guidelines For Clinical Trials

Author: Jonathan J. Shuster
Publisher: CRC Press
ISBN: 9780849335426
Size: 32.98 MB
Format: PDF, Kindle
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The aim of this unique volume is to help medical researchers design clinical trials to improve survival, remission duration, or time to recurrence of disease. Written in a user-friendly step-by-step format, this work enables the researcher-with no background in statistics-to determine sample size and write statistical considerations for their protocols. It provides critical language which can help with FDA submissions and/or research grants. It also provides the mathematical justification of the material at a level consistent with one year of undergraduate mathematical statistics. It presents survival analysis methods at a more elementary level than any known text. Filled with tables, figures, plus an extensive appendix, this one-of-a-kind reference is an absolute must for all clinical researchers and biostatisticians.

Exact Statistical Methods For Data Analysis

Author: Samaradasa Weerahandi
Publisher: Springer Science & Business Media
ISBN: 9780387406213
Size: 69.80 MB
Format: PDF, Docs
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Now available in paperback. This book covers some recent developments in statistical inference. The author's main aim is to develop a theory of generalized p-values and generalized confidence intervals and to show how these concepts may be used to make exact statistical inferences in a variety of practical applications. In particular, they provide methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances. The generalized procedures are shown to be more powerful in detecting significant experimental results and in avoiding misleading conclusions.

Statistics For Evidence Based Practice In Nursing

Author: MyoungJin Kim
Publisher: Jones & Bartlett Publishers
ISBN: 1449645674
Size: 69.94 MB
Format: PDF, ePub, Mobi
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Statistics for Evidence-Based Practice in Nursing is an accessible and comprehensive learning tool for nurses returning to graduate school or in a professional role. Peer reviewed and course tested, this text presents statistics in a readable, user-friendly format to meet the learning needs of students. The text includes key terms, critical thinking questions, and case studies incorporating research and evidence-based practice to help nurses connect statistics with everyday work in the healthcare field. Key Features: * Screenshots throughout each chapter guide students through applying statistics using SPSS * Key terms serve as a tool to guide and focus study * Critical Thinking Questions allow students to apply what they have learned * Self-Quizzes reinforce key concepts at the end of each chapter Accompanied by Instructor Resources: * Save time with a Test Bank * Plan classroom lectures using PowerPoint Presentations created for each chapter * Review answers to Critical Thinking Questions and Self-Quizzes found in the text

Statistical Evidence

Author: Richard Royall
Publisher: CRC Press
ISBN: 9780412044113
Size: 58.43 MB
Format: PDF, Mobi
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Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.