The efficient planning of each system requires an understanding of the true nature of the system. Regression models also represent a system that its input variables, the role of the system input, its variables coefficients, the role of the system process, and its response variables, play the role of the system output. Therefore, for exact regression modeling and efficient planning, it's essential that the components of the model, including variables, target circles, and constraints, are correctly identified and defined. One method of the regression modeling is the design of experiments and the response surface method that are widely used to identify complex systems. In the present research, we intend to introduce a guidelines to improve the determination of the experiment range of control variables and measure the effect of experiment range of control variables to efficiency of regression modeling. The results showed the positive role of introduced guidelines and high effect of range of control variables to efficiency of regression modeling in the form of numerical results that are an approach for experts of mathematical and regression modeling, design of experiments, and response surface methodology.

Keywords: System, Regression Modeling, Experiment range, control variables, Design of Experiments, Response Surface Method.

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