Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

Statistical modelling of graphical structures provides a principled framework for representing complex dependencies among multiple variables by means of graphs. In these representations, nodes ...

A new Smart Partial Least Squares model shows that internal technological and managerial factors together with external ...

Statistical modelling of latent variables refers to the suite of methods used to infer unobserved constructs from observed data. By representing underlying factors, traits or processes that cannot be ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

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 ...

Fine particulate matter, the invisible haze of particles smaller than 2.5 microns that drifts from traffic, industry, and wildfire smoke, has long been linked to respiratory and cardiovascular ...

Predictive analytics allows data professionals to identify trends, forecast outcomes and test assumptions using data. When these capabilities are applied to simulation modeling, they make models more ...

Interview with Dr. Caroline Buckee on the uses — and limitations — of epidemiologic modeling to predict the spread of Covid-19. 10m 49s Download Amid enormous uncertainty about the future of the Covid ...

This introductory course is designed to give students the basic skills to organize and summarize data, along with an introduction to the fundamental principles of statistical inference. The course ...