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 ...

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 ...

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 ...

Statistical modelling of zero-inflated count data addresses datasets in which the frequency of zero outcomes exceeds that predicted by standard count distributions. Such phenomena arise across ...

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 ...

In the 21st century, artificial intelligence (AI) has emerged as a valuable approach in data science and a growing influence in medical research, 4-6 with an accelerating pace of innovation. This ...

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 ...

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 ...