DEFINITION of 'Regression'
A statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables).
VIDEO
BREAKING DOWN 'Regression'
The two basic types of regression are linear regression and multiple regression. Linear regression uses one independent variable to explain and/or predict the outcome of Y, while multiple regression uses two or more independent variables to predict the outcome. The general form of each type of regression is:
Linear Regression: Y = a + bX + u
Multiple Regression: Y = a + b_{1}X_{1 +} b_{2}X_{2} + B_{3}X_{3} + ... + B_{t}X_{t} + u
Where:
Y= the variable that we are trying to predict
X= the variable that we are using to predict Y
a= the intercept
b= the slope
u= the regression residual.
In multiple regression the separate variables are differentiated by using subscripted numbers.
Regression takes a group of random variables, thought to be predicting Y, and tries to find a mathematical relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all the individual data points. Regression is often used to determine how much specific factors such as the price of a commodity, interest rates, particular industries or sectors influence the price movement of an asset.

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