Regression

Regression

Linear regression is used to explain and/or predict. The general form is:

Y = a + bX + u

Where Y is the variable that we are trying to predict, X is the variable that we are using to predict Y, a is the intercept, b is the slope, and u is the regression residual.

Notes:

Regression takes a group of random variables, thought to be predicting Y, and tries to find a relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all of the X to Y.

See also: Kappa

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Regression

A mathematical technique used to explain and/or predict. The general form is Y = a + bX + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope, and u is the regression residual. The a and b are chosen in a way to minimize the squared sum of the residuals. The ability to fit or explain is measured by the R-square.