Mixture Design Tutorial (Part 1 â The Basics) - Statease.info

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ANOVA report with annotations toggled onThe statistics look very good. The model has a high F value, low probability values(Prob > F). The probability values show the significance of each term. Because themixture model does not contain an intercept term, the main effect coefficients (linearterms) incorporate the overall average response and are tested together. Scroll down tothe next report with R-squared statistics.R-squared and other statistics after the ANOVAThese statistics, many of these you’ve seen already in the “Model Summary Statistics”table, all look good. Note the more than adequate precision value of 27.9.12 • Mixture Tutorial – Part 1 Design-Expert 7 User’s Guide
DX7-05A-Mix-P1.doc Rev. 3/14/06Scroll down and take a look at the coefficients and associated confidence intervals forthe quadratic model.Coefficients for the quadratic modelContinue on to see several alternative models that vary by how the components arecoded. This may be of most interest to students of mixture design rather than actualformulators, so it’s time to move on: Press the next button – Diagnostics.Diagnose the Statistical Properties of the ModelThe most important diagnostic, the normal probability plot of the residuals, comes up bydefault.Normal Probability Plot of the ResidualsThe data points should be approximately linear. A non-linear pattern (look for an S-shaped curve) indicates non-normality in the error term, which may be corrected by atransformation. There are no signs of any problems in our data.Design-Expert 7 User’s Guide Mixture Tutorial – Part 1 • 13
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Mixture Design Tutorial (Part 1 â The Basics) - Statease.info