How to Calculate Multiple Linear Regression for Six Sigma
What should Six Sigma practitioners do with all the situations where more than one X influences a Y? You use multiple linear regression. After all, that kind of situation is more common than a single influencing variable is. When you work to create an equation that includes more than one variable — such as Y = f(X_{1}, X_{2}, . . ., X_{n}).
The general form of the multiple linear regression model is simply an extension of the simple linear regression model For example, if you have a system where X_{1} and X_{2} both contribute to Y, the multiple linear regression model becomes
Y_{i} = β_{0} + β_{1}X_{1} + β_{11}X_{1}^{2} + β_{2}X_{2} + β_{22}X_{2}^{2} + β_{12}X_{1}X_{2} + ε
This equation features five distinct kinds of terms:

β_{0}: This term is the overall effect. It sets the starting level for all the other effects, regardless of what the X variables are set at.

β_{i}X_{i}: The β_{1}X_{1} and β_{2}X_{2} pieces are the main effects terms in the equation. Just like in the simple linear regression model, these terms capture the linear effect each X_{i} has on the output Y. The magnitude and direction of each of these effects are captured in the associated β_{i} coefficients.

β_{ii}X_{i}^{2}: β_{11}X_{1}^{2} and β_{22}X_{2}^{2} are the secondorder or squared effects for each of the Xs. Because the variable is raised to the second power, the effect is quadratic rather than linear. The magnitude and direction of each of these secondorder effects are indicated by the associated β_{ii} coefficients.

β_{12}X_{1}X_{2}: This effect is called the interaction effect. This term allows the input variables to have an interactive or combined effect on the outcome Y. Once again, the magnitude and direction of the interaction effect are captured in the β_{12} coefficient.

ε: This term accounts for all the random variation that the other terms can’t explain. ε is a normal distribution with its center at zero.
The equation for multiple linear regression can fit much more than a simple line; it can accommodate curves, threedimensional surfaces, and even abstract relationships in ndimensional space! Multiple linear regression can handle about anything you throw at it. The process for performing multiple linear regression follows the same pattern that simple linear regression does:

Gather the data for the Xs and the Y.

Estimate the multiple linear regression coefficients.
When you have more than one X variable, the equations for deriving the βs become very complex and very tedious. You definitely want to use a statistical analysis software tool to calculate these equations automatically for you. The βs just pop right out. Otherwise, go buy a box of number 2 pencils and roll up your sleeves!

Check the residual values to confirm that they meet the upfront assumptions of the multiple linear regression model.
Checking that the residuals are normal is critically important. If the variation of the residuals isn’t centered on zero and the variation isn’t random and normal, the starting assumptions of the multiple linear regression model haven’t been met, and the model is invalid.

Perform statistical tests to see which terms of the multiple linear regression equation terms are significant (and should be kept in the model) and which are insignificant (and need to be removed).
Some terms in the multiple regression equation aren’t significant. You find out which ones by performing an F test for each term in the equation. When the variation contribution of an equation term is small compared to the residual variation, that term won’t pass the F test, and you can remove it from the equation.
Your goal is to simplify the regression equation as much as possible while maximizing the R^{2} metric of fit. Generally, simpler is always better. So if you find two regression equations that both have the same R^{2} value, you want to settle on the one with the fewest, simplest terms.
Usually, the higher order terms are the first to go. There’s just less chance of a squared term or an interaction term being statistically significant.

Calculate the final coefficient of determination R^{2} for the multiple linear regression model.
Use the R^{2} metric to quantify how much of the observed variation your final equation explains.
With good analysis software becoming more accessible, the power of multiple linear regression is available to a growing audience. Many more sophisticated statistical analysis software tools even have automated algorithms that search through the various combinations of equation terms while maximizing R^{2}.