Specify design details for Power and Sample Size for 2-Level Factorial Design

Stat > Power and Sample Size > 2-Level Factorial Design > Design
Number of blocks
Enter a nonnegative integer for the number of blocks in the design. The number of blocks must divide evenly into the product of the number of corner points and the number of replicates. If you specify the number of replicates, this property must be true for all the numbers of replicates. If you leave the number of replicates blank, then the results have this property. To determine the maximum number of blocks for a single replicate of a design, go to Available 2-level factorial designs. For complicated cases, you can determine the valid numbers of blocks by creating the design in Minitab.
Number of terms omitted from model
Enter a nonnegative integer. Terms are omitted from the largest model that can be fit. In the largest model, the number of terms and the number of unique corner points are the same. If the design has 1 replicate and 0 center points, then you must omit at least 1 term from the model. If the design has 1 replicate, 1 center point, and you include the term for center points, then you must omit at least 1 term from the model. In other cases, you can omit terms to do calculations for reduced models. The calculations are most conservative when you omit the fewest terms possible. For more information, go to How to calculate the number of terms to omit from the model for power and sample size calculations for a 2-level factorial design.
Include term for center points in model
Usually, you include a term for center points when you have center points in the design. The center points term separates the variation due to curvature from the variation the the model does not explain. This separation produces a more accurate estimate of the variation the model does not explain when the variation that the curvature explains is non-zero. You might exclude the term for center points if you will not use the center points to model variation due to curvature. For example, you replicate the center points to provide an estimate of pure error when you cannot replicate all of the corner points.
Include blocks in model
Usually, you include blocks in the model if you plan to collect data in blocks. You can exclude blocks from the model to ignore any block effect. For example, if you already analyzed the design and the block effect was not statistically significant or if you want to see the effect on the calculations when you ignore blocks. For more information on what a block is, go to What is a block?.