Data considerations for Analyze Mixture Design

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data must include at least 2 components

To define a mixture experiment in Minitab, you must have at least two components that are continuous. If you do not have at least two components, you don't have a mixture.

The response variable should be continuous

If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.

• If your response variables has two categories, use Fit Binary Logistic Regression.
• If your response variable counts occurrences, such as number of defects, use Fit Poisson Model.

Ensure that the measurement system produces reliable response data

If the variability in your measurement system is too great, your experiment may lack the power to find important effects.

Each observation should be independent from all other observations

If your individual observations are dependent, your results might not be valid. Consider the following points to determine whether your observations are independent:

• If an observation provides no information about the value of another observation, the observations are independent.
• If an observation provides information about another observation, the observations are dependent.

The experimental runs should be randomized

Randomization reduces the chance that uncontrolled conditions will bias the results. Randomization also lets you estimate the inherent variation in materials and conditions so that you can make valid statistical inferences based on the data from your experiment.

Collect data using best practices

To ensure that your results are valid, consider the following guidelines:

• Make certain that the data represent the population of interest.
• Collect enough data to provide the necessary precision.
• Record the data in the order it was collected.

The model should provide a good fit to the data

If the model does not fit the data, the results can be misleading. In the output, use the residual plots, the diagnostic statistics for unusual observations, and the model summary statistics to determine how well the model fits the data.