Overview of Quick Designs

Quick Designs offers a subset of the designed experiments in Minitab Statistical Software that cover common and important analytics cases. Quick Designs collects a small amount of information from you to select a design. Then, Quick Designs asks you to fill in the last information that is necessary to create a worksheet for the design. Leaving out many of the choices to consider in the full set of designs in Minitab Statistical Software lets you create common designs faster and with fewer decisions.

The subset of designs in Quick Designs includes common factorial designs and 5 designs that are useful for specific cases. Select the following links for more information about the types of designs.

Common factorial designs: Estimate main and interaction effects. For more information, go to Factorial and fractional factorial designs.
Screening designs: Efficiently identify the most significant variables among many potential candidates. For more information, go to Screening designs.
Response surface designs: Estimate main, interaction, and quadratic effects. For more information, go to Response surface designs.
2-level split-plot design: Estimate main and interaction effects when one or more factors are hard to change. For more information, go to Split-plot designs.
Mixture designs: Understand the effect of changes in the proportions of components of a mixture. For more information, go to Mixture designs.
Taguchi design: Find the optimal factor settings to achieve robustness to uncontrollable noise. For more information, go to Taguchi designs.

Design of experiments: How many factors do you want to study?

First, Quick Designs asks for the number of factors to study: 2, 3, 4, 5, 6 or more.

For 2 to 6 factors, select the number of factors to study.
For 7 or more factors, select Select a Screening Design.

Decision details

The following information provides information on factors and screening designs.

What is a factor?: Factors are predictor variables (also called independent variables) which you choose to systematically vary during an experiment to determine their effect on the response (dependent) variable.; For example, you want to inspect the surface finish of metal parts. For the application of interest, you want to evaluate how feed rate, cutting speed, and cut depth affect the finished product. Feed rate, cutting speed, and cut depth are the factors in the experiment. In the experiment, you intentionally vary the values of the factors.; At this phase of Quick Designs, factors include mixture components. Components are the ingredients that make up a mixture. In a mixture, the proportions of the components influence the response. Suppose you want to study how the proportions of three components in a household deodorizer affect the acceptance of the product based on scent. The three components are rose oil, tangerine oil, and neroli oil. In the experiment, you intentionally vary the proportions of the oils in the mixture.
What is a screening design?: For more than 7 factors, Quick Designs guides you to a screening design.; In many applications, the number of factors that potentially affect process quality is too great to study all factors in detail. The usual goal of a screening design is to identify the most important factors that affect process quality. After screening experiments, you usually do optimization experiments that provide more detail on the relationships among the most important factors and the response variables.

Overview of Quick Designs

Design of experiments: How many factors do you want to study?

Decision details

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