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| Regression |
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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).
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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 + b1X1 + b2X2 + B3X3 + ... + BtXt + 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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