Figure 10.67. Parameters from multiplicative effect in GEMANOVA model. The estimated response at specified levels of the four factors equal the product of the corresponding effects plus the muscle term, which varies between 31 and 33. |

The higher-level parameters in the multiple regression model enable quantification of how chemicals influence each other in relation to the measured response. Suppose that fil 2 (the estimated function parameter for the first-level interaction term between chemical 1 and chemical 2) in a regression model [Pg.137]

Changing the exit gas pressure also gave three multiple steady state responses in which the same set of operating parameters produced different reactor profiles. Finally, a rough estimate for the location of the bifurcation points was given for the coal moisture, steam feed rate, and exit gas pressure transient response runs. [Pg.364]

These parameters can be estimated by multiple linear regression. This method is described below. By this procedure, the polynomial model is fitted to known experimental results so that the deviations between the observed responses and the corresponding responses calculated from the model are as small as possible. How these calculations are done and how the experiments should be laid out to obtain good estimates of the model parameters is treated in detail in the chapters that follow. [Pg.35]

To be of any practical value, a response surface model should give a satisfactory description of the variation of y in the experimental domain. This means that the model error R(x) should be negligible, compared to the experimental error. By multiple linear regression, least squares estimates of the model parameters would minimize the model error. Model fitting by least squares multiple linear regression is described in the next section. [Pg.50]

From this conclusion follows, that a factorial design can be used to fit a response surface model to account for main effects and interaction effects. In the concluding section of this chapter is discussed how the properties of the model matrix X influence the quality of the estimated parameters in multiple regression. It is shown that factorial design have optimum qualities. [Pg.105]

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