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Design of experiments (DOE) is one of the most widely used experimental designs because of its low cost and time saving. DOE involves four main stages of planning, experiment design, analyze results and interpreting the experimental results. The central concept of DOE is that the design of experiments is an experimental design and an analysis of the results of experiments in a way that aims to estimate and predict the values of the responses of interest.[16] In general, DOE is used for experimental design of optimization models and statistical models. A typical experiment, or experimental design, may include a finite range of values for the independent variable, a nominal value of the independent variable, and a set of measured values of the dependent variable for a set of experiments. To collect the range of values for independent variables, a central composite design is often used. To collect nominal values for independent variables, all possible variants of the independent variables are designed using an appropriate coding scheme. To estimate and predict the values of the responses of interest, a regression analysis of the collected experimental data is performed.[16] Although a full-factorial design with all possible conditions is theoretically the most ideal design for a given response, it is rarely possible to obtain enough data to perform a full-factorial experiment. Therefore, alternative designs like factorial design are frequently used to determine the effects of the factors and their interactions. Factor cost functions were designed using the variance weighted distance weighting method that can be used to determine the appropriate number of experiments needed to meet desired study objectives.[16] The SPSS software package was utilized to generate and perform experimental design in our work.
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