The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

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 ...

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 ...

In the early 1970s, statisticians had difficulty in analysing data where the random variation of the errors did not come from the bell-shaped normal distribution. Besides normality, these traditional ...