PROPHET StatGuide: Multiple Linear Regression

Multiple linear regression fits a response variable as a linear combination of multiple X variables by the method of least squares.


The normality assumption is required for hypothesis tests, but not for estimation.
The X variables are also known as the independent variables.
The Y variable is also known as the dependent variable.

The coefficients are bj, the amount by which the expected value of Y increases when Xj increases by a unit amount, when all the other X variables are held constant. This interpretation of the coefficients does not hold if some of the X variables are functions of the others, such as an interaction term Xj*Xk.

Note that it is not assumed that the X variables are independent of each other.


To properly analyze and interpret the results of multiple linear regression, you should be familiar with the following terms and concepts:

If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply multiple linear regression may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:

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Last modified: February 20, 1997

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