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 ...

Learning statistics is essential for pursuing a career in data science or analytics. Data scientists and analysts use statistics to uncover the meaning behind data.

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 ...

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 ...

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.

Multidimensional data arise when a number of different response variables are required to measure the outcome of interest. Examples of such outcomes include quality of life, cognitive ability, and ...

Programming C without variables is like, well, programming C without variables. They are so essential to the language that it doesn’t even require an analogy here. We can declare and use them… ...

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 ...