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Zusatztext 94347524 Informationen zum Autor Ken Kleinman, Nicholas J. Horton Klappentext Retaining the same accessible format of the popular first edition, this book explains how to easily perform an analytical task in both SAS and R. Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. This edition now covers RStudio, a powerful and easy-to-use interface for R. Along with extended examples of simulations, it also incorporates a number of additional topics, including APIs, reproducible analysis tools, database management systems, MCMC methods, and finite mixture models. Zusammenfassung An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website. Inhaltsverzeichnis Data Input and Output. Data Management. Statistical and Mathematical Functions. Programming and Operating System Interface. Common Statistical Procedures. Linear Regression and ANOVA. Regression Generalizations and Modeling. A Graphical Compendium. Graphical Options and Configuration. Simulation. Special Topics. Case Studies. Appendices. ...
