Statistics Toolbox

Part 2

Now analyze the experimental design data with a polynomial model and a nonlinear model, and comparing the results. The true model for the process, which is used to generate the data, is actually a nonlinear model. However, within the range of the data, a quadratic model approximates the true model quite well.

To see the polynomial model, click the Response Surface button on the Experimental Design Data window. rsmdemo calls rstool, which fits a full quadratic model to the data. Drag the reference lines to change the levels of the reactants, and find the optimal reaction rate. Observe the width of the confidence intervals.

Now click the Nonlinear Model button on the Experimental Design Data window. rsmdemo calls nlintool, which fits a Hougen-Watson model to the data. As with the quadratic model, you can drag the reference lines to change the reactant levels. Observe the reaction rate and the confidence intervals.

Compare the analysis results for the two models. Even though the true model is nonlinear, you may find that the polynomial model provides a good fit. Because polynomial models are much easier to fit and work with than nonlinear models, a polynomial model is often preferable even when modeling a nonlinear process. Keep in mind, however, that such models are unlikely to be reliable for extrapolating outside the range of the data.

Part 1

The glmdemo Demo