Regression Graphics:Ideas for Studying Regressions Through Graphics R. Dennis Cook
Applied Regression Computing Graphics: Cook, Weisberg
Applied Regression Including Computing and Graphics: R. Dennis Cook, Sanford Weisberg
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
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book´s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for ´´wide´´ data (p bigger than n), including multiple testing and false discovery rates.
Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts. Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book. * Learn basic concepts of measurement and probability theory, data management, and research design * Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher?s exact tests, and techniques for analyzing nonparametric data * Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression * Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology * Communicate with statistics and critique statistical information presented by others
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, it´s 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 you´ll learn everything you need in the book. After reading this book you´ll be able to produce graphics customized precisely for your problems, and you´ll find it easy to get graphics out of your head and on to the screen or page.