Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

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 ...

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, ...

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 ...

Regression analysis (or, more specifically, linear regression analysis) finds a "line of best fit" between a response variable and one or more explanatory variables. This applet allows users to look ...

The usual definition of R² (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an ...

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 ...

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 ...

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 ...