Regression analysis is a popular tool in finance, and the r-squared value is an essential part of that analysis.

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

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 ...

A framework is developed for the interpretation of regression plots, including plots of the response against selected covariates, residual plots, added-variable plots, and detrended added-variable ...

The R-Squared Growth Rate is a measure of how close the actual earnings come to the earnings growth on a regression basis. In other words, how closely do the earnings conform to the regression line?

Sliced inverse regression (SIR) and an associated chi-squared test for dimension have been introduced as a method for reducing the dimension of regression problems whose predictor variables are normal ...

To do this in R we must first make sure we limit our data frame to numerical variables (the regression function creates dummies automatically, but AirEntrain remains a categorical variable). To do ...

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

Kevin Matras writes about the R-Squared Growth Rate and how it can help you find outperforming stocks. Highlighted stocks include ARW, AVT and PRU.