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A response surface design is a set of advanced design of experiments (DOE) techniques that help you better understand and optimize your response. Response surface design methodology is often used to refine models after you have determined important factors using screening designs or factorial designs; especially if you suspect curvature in the ...   

Use the DOE Planning form to set up and plan experiments using two basic types of DOEs. Factors can have any number of levels. A general full-factorial experiment is always a full-factorial design. Every factor has exactly two levels. A 2 k factorial experiment is either a full-factorial or fractional-factorial.   

Open the sample data, InsulationProperties.MTW. Choose Stat > DOE > Factorial > Analyze Factorial Design. In Responses, enter Strength. Click Terms. Under Include terms in the model up through order, choose 2. Click OK and then click Covariates. In Covariates, enter MeasTemp. Click OK and then click Graphs.   

So how do you statistically determine if the relationship is linear or not? With center points! If the center point p-value is significant (i.e., less than alpha), then you can conclude that curvature exists and use response surface DOE—such as a central composite design—to analyze your data.   

When you have many factors to evaluate, the Assistant will walk you through a DOE to identify which factors matter the most (screening designs). Then the Assistant can guide you through a designed experiment to fine-tune the important factors for maximum impact (optimization designs).   

In science and in business, we need to perform experiments to identify the factors that have a significant effect. The objective of DOE is to reduce experimental costs—the number of tests—as much as possible while studying as many factors as possible to identify the important ones. Fractionating a Design   

Complete the following steps to analyze a factorial design. Key output includes the Pareto chart, p-values, the coefficients, model summary statistics, and the residual plots. Use a Pareto chart of the standardized effects to compare the relative magnitude and the statistical significance of both main and interaction effects.   

I select Stat > DOE > Factorial > Response Optimizer: In the above window, I can tell Minitab what my goal is. In this case, I want to know what input settings to use so that the moisture loss will be minimized .   

Conducting a Taguchi designed experiment can have the following steps: Choose Stat > DOE > Taguchi > Create Taguchi Design to generate a Taguchi design (orthogonal array). Each column in the orthogonal array represents a specific factor with two or more levels. Each row represents a run; the cell values identify the factor settings for the run.   

DOE in the Assistant has so many exciting aspects it’s hard to take it all in at once, but here are 5 highlights for when you plan and create a screening experiment: 1. Just-in-time guidance. If you’re lucky, you’ve had the chance to study DOE with an expert.