Polynomial Regression

Polynomial Regression models can contain one, two, or even several Independent Variables similar to that of a Multiple Regression model. However, Polynomial Regression goes further and treats the relationship between the Dependent and Independent Variable in more than a linear way. Polynomial Regression enables the Independent Variables to be present in various powers.

To use the Polynomial Regression forecasting technique:

1. Click  and open the ForecastingExamples.xls file.
   Note: The ForecastingExamples.xls file is a data example to demonstrate how the Polynomial Regression method is used. For your company’s purposes, you will have your own data available.
2. Click the Polynomial Regression sheet. 
3. Click in a cell containing data and open ForecastX by clicking . The Data Capture window appears.
4. Click the Forecast Method tab and select Polynomial Regression as your forecasting technique. 
   
5. In the Parameters section, configure the options as necessary. The following table details what each option means. 
   

   Parameter	Description
   Dependent Series	Select the dependent series of the Polynomial Regression.
   Max Degree	Determine the maximum possible degree of Independent Variables in the model. We recommend using no more than three.
   FPartial	F-value used to determine the highest degree of the Independent Variables used in the model.
   minFin	Determine the minimum F-value of an Independent Variable that is used in the model. Note: This is a Stepwise Regression parameter that can help determine the most significant Independent Variables in the model.
   maxFout	Determine the maximum F-value of an Independent Variable that is used in the model. Note: This is a Stepwise Regression parameter that can help determine the most significant Independent Variables in the model.

6. On the Reports tab, enable the Audit checkbox and ensure that all other reports are not selected.
7. Click Finish.

There also is an Advanced option: 

Enable the Remove Empty Values checkbox to run a statistical forecast on independent variables rather than taking them as inputs.