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 ...

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

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 ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...