Rigdon S.Introduction to Probability and Statistics for Data Science with R 2025

Download this torrent!

Rigdon S.Introduction to Probability and Statistics for Data Science with R 2025

Textbook in PDF format Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, Data Science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises. We use R throughout the book. Although we do cover an introduction to R, it would be helpful if students had some prior background in R. We use data extensively throughout the book. Most of the data sets are real (although at times we give small data sets to introduce a method). Many of these data sets are large. In most cases, we have provided a CSV (comma separated values) file for the data. We also provide the R code used in the book to analyze the data sets that we provide. This can be found at: cambridge.org/ProbStatsforDS. R is cutting-edge, free, open-source statistical software. R runs on a wide variety of Unix platforms, and on the Windows and MacOS operating systems. There are many good tutorials and books on R coding. We assume that the reader knows the basics of R and how it runs. In this book we will explain how R is used to analyze data while assuming some familiarity with R