Create 2-Level Factorial Design

In a 2-level factorial design, each experimental factor has only two levels. The experimental runs include all combinations of these factor levels. Although 2-level factorial designs are unable to explore fully a wide region in the factor space, they provide useful information for relatively few runs per factor. Because 2-level factorial designs can identify major trends, you can use them to provide direction for additional experimentation. For example, when you need to explore a region where you believe optimal settings may exist, you can augment a factorial design to form a central composite design.

Perform the analysis

Complete the following steps to specify the design.

Enter the name of your response variable: The worksheet includes a column with this name where you enter the data from the experiment.
Table of factors: Under Name, enter a descriptive name for each factor.; For any continuous factors, enter numbers. Enter the lower number for the study in the Low column. For example, to study the temperatures 30 and 40, enter 30 in the Low column and 40 in the High column.; For any categorical factors, enter labels for the levels. Labels can be numbers or text. If you have a text factor and the levels have no natural order, you can specify the levels in any order.
Number of replicates: Select the number of replicates for the corner points. Replicates are multiple experimental runs with the same factor settings (levels). One replicate is equivalent to the base design, where you conduct each run once. With two replicates, you perform each run twice (in random order), and so on.; Adding replicates can help increase the precision of your model and increase the power to detect effects. To determine how many replicates to include in your design, consider the available resources and the purpose of your design. For example, in a screening design or in sequential experimentation you could begin with the base design (1 replicate) and then consider whether to add replicates after you analyze the data. You can add replicates to your design later with Stat > DOE > Modify Design. For more information on replicates, go to Replicates and repeats in designed experiments.

Example

A quality engineer wants to study the influence that six input variables (factors) have on the shrinkage of a plastic fastener of a toy. The engineer plans a pilot study to screen these six factors to determine which factors have the greatest influence on the response. The engineer is primarily interested in main effects and some 2-way interactions.

Choose Stat > DOE > Quick Designs.
Select Select a Six-Factor Design.
Select Create an experiment with two or more continuous factors. Select OK.
Select Estimate main and interaction effects. Select OK.
In the new dialog, in Enter the name of your response variable, enter Shrinkage.

Complete the table with the following settings:

Name	Type	Low	High
Cooling time	Continuous	10	20
Injection pressure	Continuous	150000	250000
Injection speed	Continuous	5	10
Injection temperature	Continuous	180	360
Packing pressure	Continuous	150000	250000
Holding pressure	Continuous	150000	250000

In Number of replicates, select None. Select OK.

The design summary table shows that the design has 35 runs, which include 3 center points. The worksheet contains the 35 runs in run order, which is random.