Development of Statistical Models

DOES has the capability of developing models of products and processes including:
  • Linear Regression models
  • Multiple Regression 
  • Response Surface Models 
  • Ridge regression 

  •   1st order models 
      2nd order 
  • Higher order models 
  •    3rd order 
       4th order 
  • Orthogonal polynomials
  • Mixture (blend) models
It is important to develop models when analyzing statistically designed tests.
Without models you lose some of the valuable test information. 
The usefulness of statistical models is huge. Once a model is developed the
 model may be used to predict Ďall products within the test rangesí. Thus the model may represent thousands of products!
Models are used to plot graphs of measured responses versus design variables.

Models are used to find the optimum combination of design variables.

In this section you will find articles that help define the design of experiments operations and theory

Why Use Design of Experiments


DOES Inc. Clients Experience Staff References DOE Knowledge Services Contact


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