Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Andriy Blokhin has 5+ years of professional experience in public accounting, personal investing, and as a senior auditor with Ernst & Young. Thomas J Catalano is a CFP and Registered Investment ...

We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and quasi-likelihood contexts. In the ordinary ...

The usual approach to handling missing data in a regression is to assume that the points are missing at random (MAR) and use either a fill-in method to replace the missing points or a method using ...

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can ...

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Financial Mathematics and Statistics, BSc in Mathematics with ...