introduction to statistics through resampling methods and r

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Introduction To Statistics Through Resampling Methods And R

Author : Phillip I. Good
ISBN : 9781118497579
Genre : Mathematics
File Size : 73. 1 MB
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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.

Introduction To Statistics Through Resampling Methods And R S Plus

Author : Phillip I. Good
ISBN : 9780471722496
Genre : Mathematics
File Size : 82. 77 MB
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Introduction To Statistics Through Resampling Methods And Microsoft Office Excel

Author : Phillip I. Good
ISBN : 9780471741763
Genre : Mathematics
File Size : 38. 38 MB
Format : PDF
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Learn statistical methods quickly and easily with the discoverymethod With its emphasis on the discovery method, this publicationencourages readers to discover solutions on their own rather thansimply copy answers or apply a formula by rote. Readers quicklymaster and learn to apply statistical methods, such as bootstrap,decision trees, t-test, and permutations to better characterize,report, test, and classify their research findings. In addition totraditional methods, specialized methods are covered, allowingreaders to select and apply the most effective method for theirresearch, including: * Tests and estimation procedures for one, two, and multiplesamples * Model building * Multivariate analysis * Complex experimental design Throughout the text, Microsoft Office Excel(r) is used toillustrate new concepts and assist readers in completing exercises.An Excel Primer is included as an Appendix for readers who need tolearn or brush up on their Excel skills. Written in an informal, highly accessible style, this text is anexcellent guide to descriptive statistics, estimation, testinghypotheses, and model building. All the pedagogical tools needed tofacilitate quick learning are provided: * More than 100 exercises scattered throughout the text stimulatereaders' thinking and actively engage them in applying theirnewfound skills * Companion FTP site provides access to all data sets discussed inthe text * An Instructor's Manual is available upon request from thepublisher * Dozens of thought-provoking questions in the final chapter assistreaders in applying statistics to solve real-life problems * Helpful appendices include an index to Excel and Excel add-infunctions This text serves as an excellent introduction to statistics forstudents in all disciplines. The accessible style and focus onreal-life problem solving are perfectly suited to both students andpractitioners.

Gewu T Wie Praxisleitfaden Seriation Und Korrespondenzanalyse In Der Arch Ologie

Author : Frank Siegmund
ISBN : 9783739298016
Genre : History
File Size : 79. 43 MB
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Seriation und Korrespondenzanalyse (CA) sind statistische Verfahren, die in der Archäologie häufig angewendet werden. Die CA dient vor allem dazu, die Kombination von Objekten (Typen) in zahlreichen Fundkomplexen wie z. B. Gräbern, Horten oder Siedlungsgruben zu analysieren, um deren relative zeitliche Abfolge aufzudecken. Das Buch führt in die Methode ein und bietet vor allem auf Basis der freien Software PAST eine praktische Einführung mit konkreten Übungsbeispielen. Der Text ist zum Selbststudium gedacht. Nach Durcharbeiten des Leitfadens und der Übungen ist der Leser in der Lage, selbständig eigene Analysen durchzuführen und auszuwerten. Der Praxisleitfaden ist von einem Archäologen für Archäologen geschrieben und mit Studierenden der Archäologie getestet, doch die Methode ist auch für andere Wissensbereiche wie etwa die Sozialwissenschaften oder Biologie und Ökologie relevant. Grundlage der praktischen Übungen ist die freie Software PAST, die für Rechner unter MS-Windows sowie MACs verfügbar ist.

Analyzing The Large Number Of Variables In Biomedical And Satellite Imagery

Author : Phillip I. Good
ISBN : 1118002148
Genre : Mathematics
File Size : 68. 18 MB
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This book grew out of an online interactive offered through statcourse.com, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological context as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though fully knowledgeable concerning the procedures used to generate microaarrays, EEGs, or MRIs, needed a full introduction to the resampling methods—the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step-by-step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees.

Tracking Environmental Change Using Lake Sediments

Author : John B.H. Birks
ISBN : 9789400727441
Genre : Science
File Size : 27. 92 MB
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Numerical and statistical methods have rapidly become part of a palaeolimnologist’s tool-kit. They are used to explore and summarise complex data, reconstruct past environmental variables from fossil assemblages, and test competing hypotheses about the causes of observed changes in lake biota through history. This book brings together a wide array of numerical and statistical techniques currently available for use in palaeolimnology and other branches of palaeoecology. ​ Visit http://extras.springer.com the Springer's Extras website to view data-sets, figures, software, and R scripts used or mentioned in this book.

Resampling Methods

Author : Phillip I. Good
ISBN : 0817642439
Genre : Mathematics
File Size : 68. 49 MB
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"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Good attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well."- Ron C. Fryxell, Albion College"...The wealth of the bibliography covers a wide range of disciplines."---Dr. Dimitris Karlis, Athens University of EconomicsThis thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in science, engineering, and technology.Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.Topics and Features:* Offers more practical examples plus an additional chapter dedicated to regression and data mining techniques and their limitations* Uses resampling approach to introduction statistics* A practical presentation that covers all three sampling methods: bootstrap, density-estimation, and permutations* Includes systematic guide to help one select the correct procedure for a particular application* Detailed coverage of all three statistical methodologies: classification, estimation, and hypothesis testing* Suitable for classroom use and individual, self-study purposes* Numerous practical examples using popular computer programs such as SAS(r), Stata(r), and StatXact(r)* Useful appendixes with computer programs and code to develop individualized methods* Downloadable freeware from author's website: http://users.oco.net/drphilgood/resamp.htmWith its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests. Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.

Common Errors In Statistics And How To Avoid Them

Author : Phillip I. Good
ISBN : 9781118211274
Genre : Mathematics
File Size : 83. 95 MB
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Praise for the Second Edition "All statistics students and teachers will find in this book afriendly and intelligentguide to . . . applied statistics inpractice." —Journal of Applied Statistics ". . . a very engaging and valuable book for all who usestatistics in any setting." —CHOICE ". . . a concise guide to the basics of statistics, replete withexamples . . . a valuablereference for more advanced statisticiansas well." —MAA Reviews Now in its Third Edition, the highly readable CommonErrors in Statistics (and How to Avoid Them) continues to serveas a thorough and straightforward discussion of basic statisticalmethods, presentations, approaches, and modeling techniques.Further enriched with new examples and counterexamples from thelatest research as well as added coverage of relevant topics, thisnew edition of the benchmark book addresses popular mistakes oftenmade in data collection and provides an indispensable guide toaccurate statistical analysis and reporting. The authors' emphasison careful practice, combined with a focus on the development ofsolutions, reveals the true value of statistics when appliedcorrectly in any area of research. The Third Edition has been considerably expanded andrevised to include: A new chapter on data quality assessment A new chapter on correlated data An expanded chapter on data analysis covering categorical andordinal data, continuous measurements, and time-to-event data,including sections on factorial and crossover designs Revamped exercises with a stronger emphasis on solutions An extended chapter on report preparation New sections on factor analysis as well as Poisson and negativebinomial regression Providing valuable, up-to-date information in the sameuser-friendly format as its predecessor, Common Errors inStatistics (and How to Avoid Them), Third Edition is anexcellent book for students and professionals in industry,government, medicine, and the social sciences.

Mathe Manga Statistik

Author : Shin Takahashi
ISBN : 3834805661
Genre : Mathematics
File Size : 85. 95 MB
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Statistik ist trocken und macht keinen Spaß? Falsch! Mit diesem Manga lernt man die Grundlagen der Statistik kennen, kann sie in zahlreichen Aufgaben anwenden und anhand der Lösungen seinen Lernfortschritt überprüfen – und hat auch noch eine Menge Spaß dabei! Eigentlich will die Schülerin Rui nur einen Arbeitskollegen ihres Vaters beeindrucken und nimmt daher Nachhilfe in Statistik. Doch schnell bemerkt auch sie, wie interessant Statistik sein kann, wenn man beispielsweise Statistiken über Nudelsuppen erstellt. Nur ihren Lehrer hatte sich Rui etwas anders vorgestellt, er scheint ein langweiliger Streber zu sein – oder?

Mathematical Statistics With Resampling And R

Author : Laura M. Chihara
ISBN : 9781118518953
Genre : Mathematics
File Size : 80. 83 MB
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This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

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