Zur graphischen Praesentation von Regressionsdaten gibt es seit dem Vormarsch der Computertechnik vielfaeltige neue Moeglichkeiten, die ueber die klassischen Ansaetze hinausgehen. Der Autor betritt mit seinen Ideen haeufig Neuland er illustriert sie mit zahlre
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 subje
Regression Graphics:Ideas for Studying Regressions Through Graphics R. Dennis Cook
Mit Hilfe der Regressionsanalyse versucht man, einen Zusammenhang zwischen einer Systemantwort und einer oder mehreren bestimmenden Variablen zu finden. In den letzten Jahren wurden einige neue Regressionsverfahren entwickelt, die dieses Buch behandelt un
A step-by-step guide to computing and graphics in regressionanalysisIn this unique book, leading statisticians Dennis Cook and SanfordWeisberg expertly blend regression fundamentals and cutting-edgegraphical techniques. They combine and up- date most of t
Applied Regression Including Computing and Graphics: R. Dennis Cook, Sanford Weisberg
Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic breakdown of the me
This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, its easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems, and youll find it easy to get graphics out of your head and on to the screen or page. Hadley Wickham is Chief Scientist at RStudio and Assistant Professor of Statistics at Rice University. Hadley is interested in developing computational and cognitive tools for making data preparation, visualization, and analysis easier. He has developed 15 R packages and in 2006 won the John Chambers Award for Statistical Computing for his work on the ggplot and reshape R packages. Carson Sievert is a PhD student in the Department of Statistics at Iowa State University. His work includes R packages for acquiring data from the Web (pitchRx, bbscrapeR, XML2R), designing interactive Web graphics (animint, plotly), and visualizations for exploring statistical models (LDAvis).
With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you´re a beginner, R Cookbook will help get you started. If you´re an experienced data programmer, it will jog your memory and expand your horizons. You´ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data ´´Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language - one practical example at a time.´´ - Jeffrey Ryan, software consultant and R package author
The aim of this textbook (previously titled SAS for Data Analytics ) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: - Covers SAS v9.2 and incorporates new commands - Uses SAS ODS (output delivery system) for reproduction of tables and graphics output - Presents new commands needed to produce ODS output - All chapters rewritten for clarity - New and updated examples throughout - All SAS outputs are new and updated, including graphics - More exercises and problems - Completely new chapter on analysis of nonlinear and generalized linear models - Completely new appendix Mervyn G. Marasinghe, PhD , is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD , is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.