Linear regression (also called simple regression) is one of the most common techniques of regression analysis. Multiple regression is a broader class of regression analysis, which encompasses both ...

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 ...

The topic of variable importance in linear regression is reviewed, and a measure first justified theoretically by Pratt (1987) is examined in detail. Asymptotic variance estimates are used to ...

If you want to advance your data science skill set, Python can be a valuable tool for SEOs to generate deep data insights to help your brand. The programming language of Python is gaining popularity ...

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 ...

This paper provides an alternative approach to penalized regression for model selection in the context of high-dimensional linear regressions where the number of covariates is large, often much larger ...

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 ...