For example, if X and Y are highly correlated, they could be replaced in a linear regression by X+Y and X-Y without changing the fit of the model or statistics for other regressors.

Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Additive models and tree-based regression models are two main classes of statistical models used to predict the scores on a continuous response variable. It is known that additive models become very ...

This example shows how to build a menu system that enables you to perform nonlinear regression from a menu. Six modules are stored on an IML storage disk. After you have stored them, use this example ...

Hilary Finucane, Brendan Bulik-Sullivan, Benjamin Neale, Alkes Price and colleagues introduce a new method, called stratified LD score regression, for partitioning heritability by functional ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 15, No. 3 (Sep., 1987), pp. 209-225 (17 pages) A number of methods have been proposed for dealing with extra-Poisson ...

Predicting the Future The most common use of regression in business is to predict events that have yet to occur. Demand analysis, for example, predicts how many units consumers will purchase.

Examination of the (sample) residuals resulting from the regression analysis can indicate failures of assumptions 1, 3, and 4. Such failures are not necessarily a bad thing: They can point the way to ...