![]() But B appears not to be important either as a main effect or within any interaction. So what have we learned here? Two of these factors are clearly important, A and C. You want to test a number of factors to see which ones are important. This is a nice example to illustrate the purpose of a screening design. However the other interaction, AC is significant. In looking at the interactions, AB, is not significant, BC is not significant, and the ABC are not significant. ![]() Now we can see from these results that the A effect and C effect are highly significant. The analysis of variance shows the individual effects and the coefficients, (which are half of the effects), along with the corresponding t-tests. S = 47.4612 R-Sq = 96.61% R-Sq(adj) = 93.64%Īnalysis of Variance for EtchRate (coded units) Source From here we were able to determine which effects were significant and should remain in the model and which effects were not significant and can be removed to form a simpler reduced model.įactorial Fit: EtchRate versus A, B, C Estimated Effects and Coefficients for EtchRate (coded units) Term We began with the full model with all the terms included, both the main effects and all of the interactions. So, we again go to the Stat > DOE > Factorial Menu where we will analyze the data set from the factorial design. These response data, Yield, are the individual observations, not the totals. ![]() Once we have created a factorial design within the Minitab worksheet we then need to add the response data so that the design can be analyzed. In practice, you would want to randomize the order of run when you are designing the experiment. In the example that was shown above, we did not randomize the runs but kept them in standard order for the purpose of seeing more clearly the order of the runs. We will come back to this command another time to look at fractional factorial and other types of factorial designs. In Minitab we use the software under Stat > Design of Experiments to create our full factorial design. Once you have these contrasts, you can easily calculate the effect, you can calculate the estimated variance of the effect and the sum of squares due to the effect as well. In general for \(2^k\) factorials the effect of each factor and interaction is: How can we apply what we learned in the preceding section?
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