The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate correlation coefficient. If you look at the multiple regression we did, ...

Successful investing requires the ability to distinguish long-term trends from the short-term noise that moves stock prices on a minute-to-minute basis. One way to tune out the random oscillations and ...

Learn how to build a simple linear regression model in C++ using the least squares method. This step-by-step tutorial walks you through calculating the slope and intercept, predicting new values, and ...

Understanding one of the most important types of data analysis. by Amy Gallo You probably know by now that whenever possible you should be making data-driven decisions at work. But do you know how to ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

Statistical texts differ in the ways they test the significance of coefficients of lower-order terms in polynomial regression models. One reason for this difference is probably the concern of some ...