R-squared values range from 0 to 1, indicating the fit and explanatory power of a regression model. Values below 0.3 suggest weak explanatory power; above 0.7 indicate strong relationships. In finance ...

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 ...

Peter Frase, opens new tab uses the controversy to rail against non-academic econobloggers, or “wonks”, who parrot the findings of academics: Zach Beauchamp, opens new tab echoes Frase’s sentiment, ...

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 ...

We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the ...

Most investors have probably never heard of the R-Squared Growth Rate. And even fewer know what it means. (Ten years ago, you could count me as one of them.) But, now I do. I want you to know too. So ...

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 ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems. Logistic regression is a technique used to make ...

The American Political Science Review (APSR) is the longest running publication of the American Political Science Association (APSA). APSR, first published in November 1906 and appearing quarterly, is ...