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 ...

The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

Suzanne is a content marketer, writer, and fact-checker. She holds a Bachelor of Science in Finance degree from Bridgewater State University and helps develop content strategies. If you've ever ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

This course is compulsory on the BSc in Financial Mathematics and Statistics and BSc in Statistics with Finance. This course is available on the BSc in Actuarial Science, BSc in Business Mathematics ...

This workshop is hands-on and intended for beginners; no previous knowledge of data analysis and/or R is required. This session will cover the following topics for R: data preparation, descriptive ...

In recent columns we showed how linear regression can be used to predict a continuous dependent variable given other independent variables 1,2. When the dependent variable is categorical, a common ...

Using a hypothetical data set, the authors provide concrete examples to illustrate that canonical correlation analysis is a general linear model, subsuming other parametric procedures as special cases ...

In a transformation model h(Y) = X'β + ε for some smooth and usually monotone function h, we are often interested in the direction of β without knowing the exact form of h. We consider a projection of ...

Last month we explored how to model a simple relationship between two variables, such as the dependence of weight on height 1. In the more realistic scenario of dependence on several variables, we can ...