An R Companion to Applied Regression

Author: John Fox,Sanford Weisberg

Publisher: SAGE Publications

ISBN: 1544336462

Category: Social Science

Page: 608

View: 2054

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this new edition, and include coverage of RStudio and R Markdown.
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An R and S-Plus Companion to Applied Regression

Author: John Fox

Publisher: SAGE

ISBN: 9780761922803

Category: Mathematics

Page: 312

View: 2374

"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. " —JEFF GILL, University of Florida, Gainesville
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R für Dummies

Author: Andrie de Vries,Robert Leidenfrost

Publisher: John Wiley & Sons

ISBN: 3527812520

Category: Computers

Page: 414

View: 308

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Freakonomics

überraschende Antworten auf alltägliche Lebensfragen ; [warum wohnen Drogenhändler bei ihren Müttern? Führt mehr Polizei zu weniger Kriminalität? Sind Swimmingpools gefährlicher als Revolver? Macht gute Erziehung glücklich?]

Author: Steven D. Levitt,Stephen J. Dubner

Publisher: N.A

ISBN: 9783442154517

Category:

Page: 411

View: 6196

Sind Swimmingpools gefährlicher als Revolver? Warum betrügen Lehrer? Der preisgekrönte Wirtschaftswissenschaftler Steven D. Levitt kombiniert Statistiken, deren Zusammenführung und Gegenüberstellung auf den ersten Blick absurd erscheint, durch seine Analysetechnik aber zu zahlreichen Aha-Effekten führt. Ein äußerst unterhaltsamer Streifzug durch die Mysterien des Alltags, der uns schmunzeln lässt und stets über eindimensionales Denken hinausführt.
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Programmieren mit R

Author: Uwe Ligges

Publisher: Springer-Verlag

ISBN: 3540799982

Category: Computers

Page: 251

View: 6833

R ist eine objektorientierte und interpretierte Sprache und Programmierumgebung für Datenanalyse und Grafik. Ausführlich führt der Autor in die Grundlagen ein und vermittelt eingängig die Struktur der Sprache. So ermöglicht er Lesern den leichten Einstieg: eigene Methoden umsetzen, Objektklassen definieren und Pakete aus Funktionen und zugehöriger Dokumentation zusammenstellen. Detailliert beschreibt er die enormen Grafikfähigkeiten von R. Für alle, die R als flexibles Werkzeug zur Datenanalyse und -visualisierung einsetzen. In 2. Auflage mit vielen Verbesserungen und Neuerungen von R-2.3.x und weiteren von Lesern gewünschten Ergänzungen.
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Mathe-Manga Statistik

Author: Shin Takahashi

Publisher: Springer-Verlag

ISBN: 9783834805669

Category: Mathematics

Page: 189

View: 8510

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?
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R in a Nutshell

Author: Joseph Adler

Publisher: O'Reilly Germany

ISBN: 3897216507

Category: Computers

Page: 768

View: 9812

Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.
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Applied Regression Analysis and Generalized Linear Models

Author: John Fox

Publisher: SAGE Publications

ISBN: 1483352528

Category: Social Science

Page: 688

View: 9760

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book. Key Updates to the Second Edition: Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data Offers new chapters on missing data in regression models and on methods of model selection Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression Incorporates new examples using larger data sets Includes an extensive Web site at http://www.sagepub.com/fox that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves Intended Audience: This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis.
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Applied Regression Analysis, Linear Models, and Related Methods

Author: John Fox

Publisher: SAGE

ISBN: 9780803945401

Category: Social Science

Page: 597

View: 4757

An accessible, detailed, and up-to-date treatment of regression analysis, linear models, and closely related methods is provided in this book. Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research. It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear
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Probability and Statistics with R, Second Edition

Author: Maria Dolores Ugarte,Ana F. Militino,Alan T. Arnholt

Publisher: CRC Press

ISBN: 1466504404

Category: Mathematics

Page: 983

View: 3955

Cohesively Incorporates Statistical Theory with R Implementation Since the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs. New to the Second Edition Improvements to existing examples, problems, concepts, data, and functions New examples and exercises that use the most modern functions Coverage probability of a confidence interval and model validation Highlighted R code for calculations and graph creation Gets Students Up to Date on Practical Statistical Topics Keeping pace with today’s statistical landscape, this textbook expands your students’ knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R. Web Resources A supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.
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Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 013389214X

Category: Computers

Page: 448

View: 5347

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
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Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R, Revised and Expanded Edition

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133886190

Category: Computers

Page: 384

View: 804

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
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Web and Network Data Science

Modeling Techniques in Predictive Analytics

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133887642

Category: Computers

Page: 384

View: 9119

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.
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Biophysik

Eine Einführung

Author: Rodney Cotterill

Publisher: John Wiley & Sons

ISBN: 9783527406869

Category: Biophysics

Page: 395

View: 1811

Was eignet sich besser zum Einstieg in ein neues Fachgebiet als ein in der Muttersprache verfasster Text? So manch angehender Biophysiker hätte sich den englischen 'Biophysics' von Cotterill schon lange als deutsche Übersetzung gewünscht. Hier ist sie: sorgfältig strukturiert und ausgewogen wie das englische Original, mit dem Vorzug der schnelleren Erfaßbarkeit. Vom Molekül bis zum Bewusstsein deckt der "Cotterill" alle Ebenen ab. Er setzt nur wenig Grundwissen voraus und ist damit für die Einführungsvorlesung nach dem Vordiplom ideal. Zusätzliche Anhänge mit mathematischen und physikalischen Grundlagen machen das Lehrbuch auch für Chemiker und Biologen attraktiv.
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Die Analyse kategorialer Daten

anwendungsorientierte Einführung in Logit-Modellierung und kategoriale Regression

Author: Gerhard Tutz

Publisher: De Gruyter Oldenbourg

ISBN: 9783486254051

Category: Multivariate analysis

Page: 449

View: 2391

Das Werk gibt eine Einführung in die Kategoriale Regression, die auch für Anwender sehr gut geeignet ist. Neben wirtschaftswissenschaftlichen Beispielen, die zahlenmäßig dominieren, finden sich Beispiele aus Biometrie, der medizinischen Statistik, Psychologie und Demographie. Aus dem Inhalt: Einführung. Logistische Regression und Logit-Modell für binäre abhängige Größen. Schätzung, Modellanpassung und Einflußgrößen. Multinominale Modelle für ungeordnete Kategorien. Regression mit ordinaler abhängiger Variable. Zähldaten und die Analyse von Kontingenztafeln: das loglineare Modell. Nonparametrische Regression I: Glättungsverfahren. Nonparametrische Regression II: Klassifikations- und Regressionsbäume. Kategoriale Prognose und Diskriminanzanalyse. Elemente der Schätz- und Testtheorie. Anhang.
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SSD for R

An R Package for Analyzing Single-Subject Data

Author: Charles Auerbach PhD,Wendy Zeitlin PhD

Publisher: Oxford University Press

ISBN: 0199343616

Category: Social Science

Page: 192

View: 8679

Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R-the open-source statistical programming language, written by the book's authors, Charles Auerbach and Wendy Zeitlin. SSD for R has numerous graphing and charting functions to conduct robust visual analysis. Besides the ability to create simple line graphs, additional features are available to add mean, median and standard deviation lines across phases to help better visualize change over time. This book also contains numerous tests of statistical significance, such as t-tests, chi-squares and the conservative dual criteria. Auerbach and Zeitlin guide readers through the analytical process based on the characteristics of their data. Several examples and illustrations are provided throughout to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation. SSD for R is the only book of its kind to describe single-subject data analysis while providing free statistical software to do so. Additionally, the authors have an active website (http://ssdanalysis.com) with a growing number of instructional videos and a blog to build a community of researchers interested in single-subject designs.
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Multiple and Generalized Nonparametric Regression

Author: John Fox

Publisher: SAGE Publications, Incorporated

ISBN: N.A

Category: Social Science

Page: 83

View: 5837

This book builds on John Fox’s previous volume in the QASS Series, Non Parametric Simple Regression. In this book, the reader learns how to estimate and plot smooth functions when there are multiple independent variables.
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Using the R Commander

A Point-and-Click Interface for R

Author: John Fox

Publisher: CRC Press

ISBN: 1498741916

Category: Mathematics

Page: 233

View: 1851

This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle. tinyurl.com/RcmdrBook The site includes data files used in the book and an errata list. http://socserv.mcmaster.ca/jfox/Books/RCommander/Writing-Rcmdr-Plugins.pdf Writing R Commander Plug-in Packages
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Learning R

A Step-by-Step Function Guide to Data Analysis

Author: Richard Cotton

Publisher: "O'Reilly Media, Inc."

ISBN: 1449357180

Category: Computers

Page: 400

View: 5162

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code
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