Fuzzy statistics and random variables represent a progressive fusion of traditional probability theory with the principles of fuzzy logic, enabling the treatment of imprecision and vagueness ...

The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing —class time ...

By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis.

We propose a ranking-based variable selection (RBVS) technique that identifies important variables influencing the response in high-dimensional data. RBVS uses subsampling to identify the covariates ...

Continuous and factor explanatory variables are both important in linear regressions. To fit a linear model using factor variables, the traditional implementation of the least squares approach defines ...

The episode also highlights the crucial importance of post-publication peer review and data audits in high-pressure research.

It's unlikely to surprise anybody who reads this website closely or regularly that different groups of students succeed in higher education at widely varying rates. New federal data, however, ...

The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models ...

The latest monthly update to the Python extension for Visual Studio Code makes it easier for developers to keep track of variables and their data when working with the ever-popular programming ...