Search Results for "an-r-and-s-plus-companion-to-applied-regression"

An R and S-Plus Companion to Applied Regression

An R and S-Plus Companion to Applied Regression

  • Author: John Fox
  • Publisher: SAGE
  • ISBN: 9780761922803
  • Category: Mathematics
  • Page: 312
  • View: 6929
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"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

An R Companion to Applied Regression

An R Companion to Applied Regression

  • Author: John Fox,Sanford Weisberg
  • Publisher: SAGE Publications
  • ISBN: 141297514X
  • Category: Social Science
  • Page: 449
  • View: 1445
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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.

Applied Regression Analysis, Linear Models, and Related Methods

Applied Regression Analysis, Linear Models, and Related Methods

  • Author: John Fox
  • Publisher: SAGE
  • ISBN: 9780803945401
  • Category: Social Science
  • Page: 597
  • View: 2458
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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

Modeling Techniques in Predictive Analytics with Python and R

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: 1178
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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

Programmieren mit R

Programmieren mit R

  • Author: Uwe Ligges
  • Publisher: Springer-Verlag
  • ISBN: 3540799982
  • Category: Computers
  • Page: 251
  • View: 4821
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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.

Parametrische Statistik

Parametrische Statistik

Verteilungen, maximum likelihood und GLM in R

  • Author: Carsten F. Dormann
  • Publisher: Springer-Verlag
  • ISBN: 3662546841
  • Category: Medical
  • Page: 363
  • View: 8924
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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.

Web and Network Data Science

Web and Network Data Science

Modeling Techniques in Predictive Analytics

  • Author: Thomas W. Miller
  • Publisher: FT Press
  • ISBN: 0133887642
  • Category: Computers
  • Page: 384
  • View: 8699
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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.

Modeling Techniques in Predictive Analytics

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: 446
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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

Multivariate Statistik

Multivariate Statistik

  • Author: Rainer Schlittgen
  • Publisher: Walter de Gruyter
  • ISBN: 3486710567
  • Category: Business & Economics
  • Page: 571
  • View: 1316
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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.

A Course in Statistics with R

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: 5767
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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

R in a Nutshell

R in a Nutshell

  • Author: Joseph Adler
  • Publisher: O'Reilly Germany
  • ISBN: 3897216507
  • Category: Computers
  • Page: 768
  • View: 2985
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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.

R für Dummies

R für Dummies

  • Author: Andrie de Vries,Joris Meys
  • Publisher: John Wiley & Sons
  • ISBN: 3527812520
  • Category: Computers
  • Page: 414
  • View: 2635
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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.

Statistics

Statistics

An Introduction Using R

  • Author: Michael J. Crawley
  • Publisher: John Wiley & Sons
  • ISBN: 1118941101
  • Category: Mathematics
  • Page: 360
  • View: 3484
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"...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.

Nonparametric Econometrics

Nonparametric Econometrics

Theory and Practice

  • Author: Qi Li,Jeffrey Scott Racine
  • Publisher: Princeton University Press
  • ISBN: 1400841062
  • Category: Business & Economics
  • Page: 768
  • View: 9880
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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.

An R and S-Plus® Companion to Multivariate Analysis

An R and S-Plus® Companion to Multivariate Analysis

  • Author: Brian S. Everitt
  • Publisher: Springer Science & Business Media
  • ISBN: 1846281245
  • Category: Mathematics
  • Page: 221
  • View: 5374
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Applied statisticians often need to perform analyses of multivariate data; for these they will typically use one of the statistical software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save users having to buy two books for the job. The author is well-known for this kind of book, and so buyers will trust that he’s got it right.

Learning R

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: 1460
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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

Probability and Statistics with R

Probability and Statistics with R

  • Author: Maria Dolores Ugarte,Ana F. Militino,Alan T. Arnholt
  • Publisher: Chapman and Hall/CRC
  • ISBN: N.A
  • Category: Mathematics
  • Page: 728
  • View: 4091
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Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning. Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables topics, including univariate and multivariate distributions. After exploring sampling distributions, the authors discuss point estimation, confidence intervals, hypothesis testing, and a wide range of nonparametric methods. With a focus on experimental design, the book also presents fixed- and random-effects models as well as randomized block and two-factor factorial designs. The final chapter describes simple and multiple regression analyses. Demonstrating that R can be used as a powerful teaching aid, this comprehensive text presents extensive treatments of data analysis using parametric and nonparametric techniques. It effectively links statistical concepts with R procedures, enabling the application of the language to the vast world of statistics.

Zeitreihenmodelle

Zeitreihenmodelle

  • Author: Andrew C. Harvey
  • Publisher: De Gruyter Oldenbourg
  • ISBN: 9783486230062
  • Category:
  • Page: 379
  • View: 2859
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Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Regression Graphics

Regression Graphics

Ideas for Studying Regressions Through Graphics

  • Author: R. Dennis Cook
  • Publisher: John Wiley & Sons
  • ISBN: 9780471193654
  • Category: Mathematics
  • Page: 349
  • View: 9823
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An exploration of regression graphics through computergraphics. Recent developments in computer technology have stimulated new andexciting uses for graphics in statistical analyses. RegressionGraphics, one of the first graduate-level textbooks on the subject,demonstrates how statisticians, both theoretical and applied, canuse these exciting innovations. After developing a relatively newregression context that requires few scope-limiting conditions,Regression Graphics guides readers through the process of analyzingregressions graphically and assessing and selecting models. Thisinnovative reference makes use of a wide range of graphical tools,including 2D and 3D scatterplots, 3D binary response plots, andscatterplot matrices. Supplemented by a companion ftp site, itfeatures numerous data sets and applied examples that are used toelucidate the theory. Other important features of this book include: * Extensive coverage of a relatively new regression context basedon dimension-reduction subspaces and sufficient summary plots * Graphical regression, an iterative visualization process forconstructing sufficient regression views * Graphics for regressions with a binary response * Graphics for model assessment, including residual plots * Net-effects plots for assessing predictor contributions * Graphics for predictor and response transformations * Inverse regression methods * Access to a Web site of supplemental plots, data sets, and 3Dcolor displays. An ideal text for students in graduate-level courses on statisticalanalysis, Regression Graphics is also an excellent reference forprofessional statisticians.

Removing the devil you know

Removing the devil you know

unraveling the puzzle behind decapitation effectiveness and terrorist group duration

  • Author: Bryan C. Price,Stanford University. Dept. of Political Science
  • Publisher: N.A
  • ISBN: N.A
  • Category:
  • Page: 652
  • View: 3643
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