This book is part of the SAS Press program.
Jorge G. Morel, Ph.D., is principal statistician for Procter and Gamble, where he specializes in SAS/STAT and SAS/IML. A SAS user since 1981, he received his Ph.D. in statistics from Iowa State University and holds two M.S. degrees in the same field. He is a member of the American Statistical Association, and he has been published widely in the Journal of Biopharmaceutical Statistics, Biometrical Journal, and Applied Statistics, among other publications.
Nagaraj K. Neerchal, Ph.D., is Professor of Statistics and Chair of the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. A SAS user since 1982, he specializes in SAS/STAT and SAS/ETS. Coauthor of more than a dozen journal articles, he received his B.S. and M.S. from the Indian Statistical Institute and his Ph.D. from Iowa State University. Dr. Neerchal received the Distinguished Achievement Award and Medal from the American Statistical Association's Section on Statistics and the Environment in 2000.
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results
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.
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 functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild 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