The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

I 'm a big fan of Python for data analysis, but even I get curious about what else is available. R has long been the go-to ...

Looking to get into statistical programming but lack industry experience? We spoke with several statistical programmers from diverse backgrounds, and one thing became clear—there’s no single path to ...

R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...

This book, “Statistical Modeling and Computation,” provides a unique introduction to modern statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of ...

Overview: Data analysis software makes sense of large amounts of information quickly.Tools like R, Python, and SPSS simplify ...

Although catastrophe theory has been applied with mixed success to many problems in the biosciences, very few of these applications have used any form of statistical modeling. We present examples of ...

If a defensive coordinator of a National Football League team could predict with high accuracy whether their team's opponent will call a pass or run play during a game, he would become a rock star in ...