About the Book
The journey from statistical model to useful output has many steps, most of which are taught in other books and courses. The purpose of this book is to focus on one particular aspect of this journey: the development and implementation of statistical algorithms. It's often nice to think about statistical models and various inferential philosophies and techniques, but when the rubber meets the road, we need an algorithm and a computer program implementation to get the results we need from a combination of our data and our models. This book is about how we fit models to data and the algorithms that we use to do so. Examples are given using the R programming language.
Please note that this book is only available in PDF form. You can view the table of contents by downloading the free sample.
About the Author
Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.