What are center points in Doe?
Center points are experimental runs with the all of the continuous factor settings set halfway between the low level and the high level. Center points tell us if the linear assumption that we use for a 2-level design is true.
How many center points should a computer based experiment have?
As a rough guide, you should generally add approximately 3 to 5 centerpoint runs to a full or fractional factorial design.
How do you calculate axial points?
To determine the local axial point, it is necessary to identify the alpha value in the CCD model. Depending on the alpha vale design can face cantered, rotatable, orthogonal. The alpha value can be calculated using the following equation: α=(2k)0.25.
What are axial points?
Filters. (mathematics) Any point that lies on any of the axes of a coordinate system.
How to calculate the sum of center points?
When using center points, the Sum of squares calculation found in D. Montgomery 5th ed, p272 multiplies the the number of center points by the number of corner points by the squarred distance between the mean of the corner points and the center points. But why do does the formula divide this sum of squares by the total number of data points used?
How to assess the significance of center points?
This is because you can not assess the significance of the center point with the same MSE that you are using for the main effects and interactions also, the center point test is a one degree of freedom test so the SS = MS. This formula makes the curvature effect equivalent to the main effects and interaction effects calculations in the ANOVA table.
Why are center points used in the lack of fit measure?
The evaluation of my design suggests that the lack of fit measure would benefit from using center points (at least one) as it currently has zero (0) df applied to it. With two categorical factors in this design, using more than one center point will add substantially to the total resources used to conduct the experiment.
How are center points used to test for curvature?
Centerpoints are a great way to test for curvature between or within factors, without fully characterizing the curvature. It will signal curvature is present, leaving the choice to you as to how to pursue characterizing the curvature. This is expecially useful if you’re following response surface methodology (method of steepest ascent/descent).