Specify the design for Create 2-Level Factorial Design (Default Generators)

Stat > DOE > Factorial > Create Factorial Design > Designs

To specify the design, select the design resolution, the number of center points, replicates, and blocks. You can use Power and Sample Size for 2-Level Factorial Design to help you to determine an appropriate number of center points and replicates.

Designs table

Minitab displays the possible base designs. Depending on the number of factors, usually, you can select a full factorial or fractional factorial design. The full factorial design lets you estimate all of the interactions between factors, but this design requires more runs. A fractional factorial design requires fewer runs but this design doesn't estimate as many interactions between factors.

The number of runs and the resolution of the total design can change based on other design elements. For example, the inclusion of center points increases the number of runs.

Number of center points per block

If you want to include center points in your design, select the appropriate number of points. You can use center points to detect curvature in the response. You can also use center points to estimate variability without having to replicate all the corner points.

Center points are runs where numeric factors are set midway between their low and high levels. For example, if a numeric factor has levels 100 and 200, the center point is set at 150. If you have text factors, then Minitab adds a center point at each level of the text factor and the midway level of the numeric factors. For example, your design includes a text factor with the levels A and B and a numeric factor with the levels 100 and 200. If you add 1 center point to the base design, Minitab adds 1 center point at levels A and 150 and 1 center point at levels B and 150. Thus, Minitab adds 2 center points for each center point that you specify.

If the design includes more than 1 block, then Minitab adds the number of center points that you specified to each block. For example, if you specify 2 center points per block and 2 blocks to your design, and the factors are numeric, Minitab adds 2 center points in block 1 and 2 center points in block 2.

Increasing the number of replicates does not add additional center points unless you also increase the number of blocks. For example, if you specify 3 center points, 2 replicates, and 1 block, then the design includes 3 center points.

For more information, go to How Minitab adds center points to a two-level factorial design.

Number of replicates for corner points

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.

Number of blocks

Select the number of blocks. If your design includes blocks, and the number of blocks equals the number of replicates, each replicate is a separate block. If the number of replicates does not equal the number of blocks, Minitab creates designs where the number of runs per block are equal.

Blocks account for the differences that might occur between runs that are performed under different conditions. For example, an engineer designs an experiment to study welding and cannot collect all of the data on the same day. Weld quality is affected by several variables that change from day-to-day that the engineer cannot control, such as relative humidity. To account for these uncontrollable variables, the engineer groups the runs performed each day into separate blocks. The blocks account for the variation from the uncontrollable variables so that these effects are not confused with the effects of the factors the engineer wants to study. For more information on how Minitab assigns runs to blocks, go to What is a block?.