Preprocess Responses/Analyze Variability Overview
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Experiments that include repeat or replicate measurements of a response allow you to analyze variability in your response data, which enables you to identify factor settings that produce less variable results. Minitab calculates and stores the standard deviations (s) of your repeat or replicate responses and analyzes them to detect differences, or dispersion effects, across factor settings.

For example, you conduct a spray-drying experiment with replicates and find settings of drying temperature and atomizer speed that produce the desired particle size. By analyzing the variability in particle size at different factor settings, you find that settings that produce particles with the least variability in size. You use multiple response optimization to find settings that produce the desired particle size with the minimum amount of variation.

Once you have created your design, analyzing variability is a two-step process:

1    Preprocess Responses - First, you calculate and store the standard deviations and counts of your repeat or replicate responses or specify standard deviations that you have already stored in the worksheet. You can analyze and graph stored standard deviations as response variables using other DOE tools, such as Analyze Variability, Analyze Factorial Design, Contour Plots, and Response Optimization.

2    Analyze Variability - Second, you fit a linear model to the log of the standard deviations you stored in the first step to identify significant dispersion effects. Once you fit a model, you can use other tools, such as contour and surface plots, and response optimization to better understand your results. You can also store weights calculated from your model to perform weighted regression when analyzing the location (mean) effects of your original responses in Analyze Factorial Design.