An R and S-Plus Companion to Applied Regression

Author: John Fox

Publisher: SAGE

ISBN: 9780761922803

Category: Mathematics

Page: 312

View: 8513

"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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An R Companion to Applied Regression

Author: John Fox,Sanford Weisberg

Publisher: SAGE Publications

ISBN: 141297514X

Category: Social Science

Page: 449

View: 2477

This book aims to provide a broad introduction to the R statistical environment in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics.
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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: 8644

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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Parametrische Statistik

Verteilungen, maximum likelihood und GLM in R

Author: Carsten F. Dormann

Publisher: Springer-Verlag

ISBN: 3662546841

Category: Medical

Page: 363

View: 570

Beispielreich baut dieses Buch Schritt für Schritt die statistischen Grundlagen moderner Datenanalysen auf. Im Gegensatz zu anderen einführenden Werken legt dieses Buch großen Wert auf einen umfassend gespannten Bogen, einen roten Faden, der alle Methoden zusammenführt. Dabei werden klassische statistische Methoden (etwa t-Test oder multiple Regression) als Spezialfall des Generalisierten Linearen Modells entwickelt. Entsprechend legt das Buch zunächst eine Grundlage in beschreibender Statistik, Verteilungen und maximum likelihood, aus der dann alle anderen Verfahren abgeleitet werden (ANOVA, multiple Regression). Jeder Schritt ist auf zwei Kapitel verteilt: Im ungradzahligen Kapitel wird anhand von vielen Beispielen und Abbildungen die Idee der statistischen Herangehensweise erläutert. Im sich daran anschließenden gradzahligen Kapitel wird die Umsetzung in der freien Statistiksoftware R gezeigt. Ein Kapitel zur Wissenschafts- und Forschungstheorie und eines zum Design von Experimenten und Stichprobeverfahren komplettiert dieses einleitende Werk. Das Buch legt großen Wert auf Verständlichkeit und Umsetzung. Mathematische Herleitungen treten demgegenüber stark in den Hintergrund. Jedes Kapitel enthält explizit ausgewiesene Lerninhalte, die durch Übungen zu jedem R-Kapitel geprüft werden können. Ein ausführliches Schlagwortverzeichnis inklusive der R-Funktionen macht das Buch auch als Nachschlagewerk nutzbar. Die zweite Auflage wurde ergänzt um Schätzung mittels der Momentenmethode, Residuendiagnostik für nicht-normalverteilte Daten und die erschöpfende Modellsuche.
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Programmieren mit R

Author: Uwe Ligges

Publisher: Springer-Verlag

ISBN: 3540267328

Category: Mathematics

Page: 237

View: 7559

R ist eine objekt-orientierte und interpretierte Sprache und Programmierumgebung für Datenanalyse und Grafik - frei erhältlich unter der GPL. Ziel dieses Buches ist es, nicht nur ausführlich in die Grundlagen der Sprache R einzuführen, sondern auch ein Verständnis der Struktur der Sprache zu vermitteln. Leicht können so eigene Methoden umgesetzt, Objektklassen definiert und ganze Pakete aus Funktionen und zugehöriger Dokumentation zusammengestellt werden. Die enormen Grafikfähigkeiten von R werden detailliert beschrieben. Das Buch richtet sich an alle, die R als flexibles Werkzeug zur Datenenalyse und -visualisierung einsetzen möchten: Studierende, die Daten in Projekten oder für ihre Diplomarbeit analysieren möchten, Forschende, die neue Methoden ausprobieren möchten, und diejenigen, die in der Wirtschaft täglich Daten aufbereiten, analysieren und anderen in komprimierter Form präsentieren.
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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: 801

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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Web and Network Data Science

Modeling Techniques in Predictive Analytics

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133887642

Category: Computers

Page: 384

View: 3643

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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Multivariate Statistik

Author: Rainer Schlittgen

Publisher: Walter de Gruyter

ISBN: 3486710567

Category: Business & Economics

Page: 571

View: 1298

Neben dem Standardkanon der wichtigen multivariaten Verfahren (Diskriminanzanalyse, Hauptkomponentenanalyse, Clusteranalyse, MDS, Faktorenanalyse, LISREL) werden in diesem Buch auch die vor allem im Marketing relevante Conjointanalyse und die PLS-Methode dargestellt. Weiterhin werden robuste Verfahren, aber auch explorative Ansätze wie das Porjection Persuit sowie Ansätze zur Behandlung von fehlenden Werten berücksichtigt. Als drittes werden auch Methoden für kategoriale Daten behandelt, die in den meisten Texten zur multivariaten Statistik noch immer eine zu geringe Beachtung erfahren. Das Augenmerk wurde im Buch durchgängig auf die Verständlichkeit der Inhalte gerichtet. Hierzu dienen die Ausführung geometrischer Konzepte und zahlreiche Beispiele.
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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: 7301

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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A Course in Statistics with R

Author: Prabhanjan N. Tattar,Suresh Ramaiah,B. G. Manjunath

Publisher: John Wiley & Sons

ISBN: 1119152739

Category: Computers

Page: 696

View: 4854

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets
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R in a Nutshell

Author: Joseph Adler

Publisher: O'Reilly Germany

ISBN: 3897216507

Category: Computers

Page: 768

View: 1960

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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An R and S-Plus® Companion to Multivariate Analysis

Author: Brian S. Everitt

Publisher: Springer Science & Business Media

ISBN: 1852338822

Category: Computers

Page: 221

View: 6973

Most data sets collected by researchers are multivariate, and in most cases, the variables need to be examined simultaneously to get the most informative results. This book covers the core multivariate methodology along with some basic theory for each method described. It also provides the necessary R and S-PLUS code for each analysis.
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Statistics

An Introduction Using R

Author: Michael J. Crawley

Publisher: John Wiley & Sons

ISBN: 1118941101

Category: Mathematics

Page: 360

View: 3267

"...I know of no better book of its kind..." (Journal ofthe Royal Statistical Society, Vol 169 (1), January2006) A revised and updated edition of this bestselling introductorytextbook to statistical analysis using the leading free softwarepackage R This new edition of a bestselling title offers a conciseintroduction to a broad array of statistical methods, at a levelthat is elementary enough to appeal to a wide range ofdisciplines. Step-by-step instructions help thenon-statistician to fully understand the methodology. Thebook covers the full range of statistical techniques likely to beneeded to analyse the data from research projects, includingelementary material like t--tests and chi--squared tests,intermediate methods like regression and analysis of variance, andmore advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within eachchapter.
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Nonparametric Econometrics

Theory and Practice

Author: Qi Li,Jeffrey Scott Racine

Publisher: Princeton University Press

ISBN: 1400841062

Category: Business & Economics

Page: 768

View: 2400

Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
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Wahrscheinlichkeitstheorie und Stochastische Prozesse

Author: Michael Mürmann

Publisher: Springer-Verlag

ISBN: 364238160X

Category: Mathematics

Page: 428

View: 6209

Dieses Lehrbuch beschäftigt sich mit den zentralen Gebieten einer maßtheoretisch orientierten Wahrscheinlichkeitstheorie im Umfang einer zweisemestrigen Vorlesung. Nach den Grundlagen werden Grenzwertsätze und schwache Konvergenz behandelt. Es folgt die Darstellung und Betrachtung der stochastischen Abhängigkeit durch die bedingte Erwartung, die mit der Radon-Nikodym-Ableitung realisiert wird. Sie wird angewandt auf die Theorie der stochastischen Prozesse, die nach der allgemeinen Konstruktion aus der Untersuchung von Martingalen und Markov-Prozessen besteht. Neu in einem Lehrbuch über allgemeine Wahrscheinlichkeitstheorie ist eine Einführung in die stochastische Analysis von Semimartingalen auf der Grundlage einer geeigneten Stetigkeitsbedingung mit Anwendungen auf die Theorie der Finanzmärkte. Das Buch enthält zahlreiche Übungen, teilweise mit Lösungen. Neben der Theorie vertiefen Anmerkungen, besonders zu mathematischen Modellen für Phänomene der Realität, das Verständnis.​
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Applied Regression Modeling

A Business Approach

Author: Iain Pardoe

Publisher: John Wiley & Sons

ISBN: 0470052651

Category: Mathematics

Page: 320

View: 2008

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression analysis to make informed decisions. Applied Regression Modeling: A Business Approach offers a practical, workable introduction to regression analysis for upper-level undergraduate business students, MBA students, and business managers, including auditors, financial analysts, retailers, economists, production managers, and professionals in manufacturing firms. The book's overall approach is strongly based on an abundant use of illustrations and graphics and uses major statistical software packages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided, although Excel does not have a built-in capability to carry out all the techniques discussed. Applied Regression Modeling: A Business Approach offers special user features, including: * A companion Web site with all the datasets used in the book, classroom presentation slides for instructors, additional problems and ideas for organizing class time around the material in the book, and supplementary instructions for popular statistical software packages. An Instructor's Solutions Manual is also available. * A generous selection of problems-many requiring computer work-in each chapter with fullyworked-out solutions * Two real-life dataset applications used repeatedly in examples throughout the book to familiarize the reader with these applications and the techniques they illustrate * A chapter containing two extended case studies to show the direct applicability of the material * A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature * More than 100 figures to aid understanding of the material Applied Regression Modeling: A Business Approach fully prepares professionals and students to apply statistical methods in their decision-making, using primarily regression analysis and modeling. To help readers understand, analyze, and interpret business data and make informed decisions in uncertain settings, many of the examples and problems use real-life data with a business focus, such as production costs, sales figures, stock prices, economic indicators, and salaries. A calculus background is not required to understand and apply the methods in the book.
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R für Dummies

Author: Andrie de Vries,Joris Meys

Publisher: John Wiley & Sons

ISBN: 3527812520

Category: Computers

Page: 414

View: 9894

Wollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.
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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: 3451

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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Biostatistical Design and Analysis Using R

A Practical Guide

Author: Murray Logan

Publisher: Wiley-Blackwell

ISBN: N.A

Category: Computers

Page: 576

View: 1564

R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.
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