1. The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs
Developed by Don Edwards, John Grego and James Lynch Center for Reliability and Quality Sciences Department of Statistics University of South Carolina

2. Part I.3 The Essentials of 2-Cubed Designs
Methodology
Cube Plots
Estimating Main Effects
Estimating Interactions (Interaction Tables and Graphs)
Statistical Significance (Effects Probability Plots)
Example With Interactions
A U-Do-It Case Study

3. Methodology Example 2
No context for example, but Minitab project can be found on the website. Create worksheet in Minitab
Looking For Patterns In The Data To Discover How The Factors Affect The Response, y.

4. Methodology Example 2 - Cube Plot
Draw backwards Z’s.

5. Methodology Example 2 - Estimating the Main Effect of A
Take the Difference of the Average Response for A+ and A-
Eliminate Those Edges of the Cube Where Going from One Cube Corner to Another Involves Changing A from - to +
Average the Corners on the Common Faces and Difference
A=[( )/4] -[( )/4] = = 5
Right vs left

6. Methodology Example 2 - Estimating the Main Effect of B
Top vs Bottom

7. Methodology Example 2 - Estimating the Main Effect of C
Front vs back

8. Methodology Example 2 - Signs Table
The Signs of the Main Effects Give The Recipes For the 8 Runs in the Design
Actual Run Corresponds to the Order of the Experimental Runs (Recipes)

9. Methodology Example 2 - Signs Table Used to Calculate Effects
To Estimate the Main Effects
Multiply the Response y by the Corresponding Sign Column
Sum the Column
Divide the Sum by the Divisor to Get the Estimated Main Effect
U-Do-It
Calculate the Main Effects Due to B and to C
U-Do-It is part of Class Exercise 3.

10. Methodology Example 2 – Partitioning A Another Way
66 to 70 would be the OVAT effect estimate. This is a good setup for ad hoc judgment of effects and insight into interaction.

11. Methodology Example 2 - Estimating the Effect of A Another Way
Average the Differences of A+and A- Over All the Combinations of B and C.
Retain Those Edges of the Cube Where Going from One Cube Corner to Another Involves Changing A from - to +
Difference These Corners and Average
[(72-68)+(71-66)+(73-66) (70-66)]/4 = 20/4 = 5
List each of 4 effects (4, 5, 7, and 4)

12. Methodology Example 2 - Estimating the Effect of the AB Interaction
Average
The Four Values in the Shaded Corners
The Four Values in the Unshaded Corners
Difference the Averages
[( )/4] -[( )/4] = =-.5
Shaded cells: A&B have the same sign. I don’t find the layout particularly intuitive.

13. Methodology Example 2 - Estimating the Effect of the AB Interaction Another Way
The Second Way Shows that the AB Interaction is Comparing the Differences in going from A- to A+ at B- and B+.
If there is a “Significant” Difference, then A and B are said to Interact
[(71-66)+(72-68)-(73-66)-(70-66)]/4 = -.5
In this example, there are no significant interactions. Thus, the interpretation is straightforward.
WARNING: When a higher order interaction is “significant,” the direct interpretation of lower order interactions and main effects is misleading.
Division by 4 seems arbitrary at first. Interpretation of lower order effects is not always misleading.

14. Methodology Example 2 - Signs Table Calculating the Signs and the Effect of Interaction AB

15. Methodology Example 2 - Signs Table U-Do-It
U-Do-It is part of CE 3.
Calculate the Signs for Interactions AC, BC and ABC
Calculate These Interaction Effects

16. Methodology Example 2 - ANOVA Table
Skim ANOVA for now

17. Methodology Example 2 - Effects Normal Probability Plot
Skim for now. The significant effects are the only ones used in computing EMR (covered a couple slides later).

18. Methodology Example 2 - Discussion
Only the Main Effect A is Significant
Set A Hi to Maximize y
Set A Lo to Minimize y
This Data is Real and Will be Considered in Later Sections
For This Data Minimizing y is the Objective
Skim parts of this

19. Methodology Example 2 - Estimating the Response
Since Only the Main Effect A is Significant The Estimated Mean Response (EMR) is given by EMR = y + (Sign of A)(Effect of A)/2 = 69 + (Sign of A)5/2
For A Lo, EMR = 69 + (-1)(2.5) = 66.5
For A Hi, EMR = 69 + (+1)(2.5) =71.5
Discuss ABC and orthogonality.

20. Methodology Example 2 - Estimating the Response Why the One-Half?
The Formula Gives You Just What You Expect: The Average Response at that Level of the A
For A Lo, EMR = 69 + (-1)(2.5) = 66.5 = ( )/4
For A Hi, EMR = 69 + (+1)(2.5) =71.5 = ( )/4

21. Use Sign Tables to Estimate Effects
II. Summary Key Ideas
Use Sign Tables to Estimate Effects
Use Probability Plots to Identify Significant Effects
Interaction Tables and Graphs are Used to Analyze Significant Interactions (To be explained later)

22. II. Summary Concluding Comments
A Main Effect Is Easy To Interpret When There Are No Significant Interactions Involving It
In The Presence of a Significant Higher-Order Interaction, the Lower-Order Interactions and Corresponding Main Effects Are Hard To Interpret by Themselves. (You Still Can Figure Out What to Do, Though)
The Size of the Effects You are Trying to Detect and the Noise of the Process (How Much Variation It Has) Will Dictate How Much Replication Is Needed