# Overview for Create 2-Level Factorial Design (Default Generators)

Use Create 2-Level Factorial Design (Default Generators) to create a designed experiment to study the effects of 2 − 15 factors. With a 2-level factorial design, you can identify important factors to focus on with further experimentation. When you create a design, Minitab stores the design information in the worksheet, which shows the order in which data should be collected. Once you have collected your data, use Analyze Factorial Design to analyze the data.

For example, a group of engineers plans an experiment to investigate the effects of three factors on the warping that occurs in a copper plate. They create a 2-level factorial design by specifying the design information, including blocks and center points, in Minitab.

This Minitab worksheet shows a portion of the design. The engineers perform the experiment by collecting data using the order shown in the RunOrder column, which contains the randomized order of the runs.
C1 C2 C3 C4 C5 C6 C7 C8
StdOrder RunOrder CenterPt Blocks Temperature Copper Content Size
12 1 0 2 75 0.8 0.50
10 2 1 2 100 1.0 0.75
8 3 1 2 50 1.0 0.25
9 4 1 2 50 0.6 0.75
11 5 0 2 75 0.8 0.50
7 6 1 2 100 0.6 0.25
2 7 1 1 100 1.0 0.25

After collecting the data, an engineer enters the response data in an empty column in the worksheet and then analyzes the design.

Many of the choices you make when you create a design depend on your overall experimental plan. For more information, go to Phases of a designed experiment.

## Where to find this analysis

Stat > DOE > Factorial > Create Factorial Design

## When to use an alternate analysis

• If you already have the factor columns in a worksheet, use Define Custom Factorial Design. Custom designs let you specify which columns are the factors and any other design columns that you already have.
• If the focus of the experiment is the main effects of the factors rather than the interactions, consider using Create Plackett-Burman Design , which is suitable for handling a large number of factors in a small number of runs. Additionally, if you have more than 15 factors, you can use a Plackett-Burman design which can fit up to 47 factors.
• To include a factor that is difficult to randomize due to time or cost constraints, use Create 2-Level Split-Plot Design.
• If you have a categorical factor with more than 2 levels, use Create General Full Factorial Design or a response surface design. You can also use a general full factorial design to create a 2-level full factorial design with 8 or more factors.
• To explore possible quadratic effects while also identifying the most important factors early in the experimentation process, use Create Definitive Screening Design.