The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs

1. The Essentials of 2-Level Design of ExperimentsPart I: The Essentials of Full Factorial Designs Developed by Don Edwards, John Grego and James LynchCenter for Reliability and Quality SciencesDepartment of StatisticsUniversity of South Carolina803-777-7800

2. Part I.3 The Essentials of 2-Cubed Designs • Methodology • Cube Plots • Estimating Main Effects • Estimating Interactions (Interaction Tables and Graphs) • Statistical Significance:When is an Effect “Real”? • An Example With Interactions • A U-Do-It Case Study • Replication • Rope Pull Exercise

3. U-Do-It ExerciseRope Pull Study* - 23 with Replication • Purpose of the Design • Test Hose to Determine the Effect of Several Factors on an Important Quality Hosiery Characteristic, Rope Pull • Response y = Upper Boot Rope Pull (in inches) • Factors: • A: Vacuum level (Lo, Hi) • B: Needle Type (EX, GB) • C: Upper Boot Speed (1000,1200) • Two Replicates of the Full 23 Were Performed*Empirical basis for this data was motivated by a much larger study performed by the developers at Sara Lee Hosiery

4. ReplicationWhy? • Average values have less variability as the number of things you average increases • Estimated effects will be reliably closer to true effects • More of the mid-sized and small effects will be distinguishable from error • Data from replicated experiments can be used to estimate the amount of variability in the process (This allows more formal test for “real” effects—ANOVA). • Data from replicated experiments can be used to determine not only which factors affect the mean of the process, but which factors affect the variability of the process.

5. ReplicationAnalysis of a Replicated 23 • Replication means repeating the entire set of 8 runs, but (for the analysis as described below), the entire collection of runs should be done in random order (be it 16, or 24, or 48, etc. runs); if you want to do them in complete sets of 8, you should analyze the results in blocks—explained later). • For our analysis, you can reduce the data to averages over each of the 8 treatment combinations; use these averages as your “y’s” in the rest of the analysis. • Discussion of shortcomings of this approach to follow • Effects plot, interaction plots, and EMR calculations are done as before using these estimated effects. Replication Example to Follow!

6. U-Do-It ExerciseRope Pull Study - Experimental Report Form

7. U-Do-It ExerciseRope Pull Study - The Analysis • To do: Analyze the data. This should include... • Fill in the table on the next slide. • Analyze the averages in Minitab: • Create a 3-factor 2-level design, enter the averages as a response variable; compute factor effects and construct a normal probability plot of the effects. • If appropriate, graph interaction plots. • Compute EMR using only the significant terms

8. U-Do-It ExerciseRope Pull Study - The Analysis

9. U-Do-It Exercise SolutionRope Pull Study • The signs table, cube plot, effects normal probability plot and AC interaction table and graph are given on the next few pages. • The cube plot leads us to expect a negative main effect for A (Vacuum level), and a positive main effect for C (upper boot speed). Note that the changes in the response for changes in A are much larger at Lo C than at Hi C, which suggests an AC interaction. Estimated effects from the response table and the normal probability plot of effects support this observation. • An AC interaction table and plot are therefore called for, and have been constructed.

10. U-Do-It Exercise SolutionRope Pull Study - Completed Cube Plot and Signs Table • Factors: • A: Vacuum Level (Lo, Hi) • B: Needle Type (EX, GB) • C: Upper Boot Speed (1000,1200) • Response: • Rope Pull (in inches)

11. U-Do-It Exercise SolutionRope Pull Study -Completed Seven Effects Paper • Factors: • A: Vacuum Level (Lo, Hi) • B: Needle Type (EX, GB) • C: Upper Boot Speed (1000,1200)

12. U-Do-It Exercise SolutionRope Pull Study -Completed AC Interaction Table

13. U-Do-It Exercise SolutionRope Pull Study - AC Interaction Plot • FactorsA: Vacuum Level (Lo, Hi) C: Upper Boot Speed (1000,1200)

14. U-Do-It Exercise SolutionRope Pull Study - Interpretation of the Experiment • There is non-ignorable interaction between A = Vacuum level and C = Upper boot speed, so we should not interpret main effects for these factors individually. For example, a Hi Vacuum level greatly increases the effect of a change from 1000 to 1200 RPM in Upper boot speed. Judging from the interaction plot, • At Lo Vacuum level, we expect a decrease of about 7” in rope pull when changing Upper boot speed from 1000 to 1200 RPM. • At Hi Vacuum level, we expect a decrease of about 14’’ in rope pull when changing Upper boot speed from 1000 to 1200 RPM. • At 1000 RPM Upper boot speed, we expect an increase of about 10” in rope pull when changing Vacuum level from Lo to Hi. • At 1200 RPM Upper boot speed, we expect an increase of about 3: in rope pull when changing Vacuum level from Lo to Hi. • The above interpretations hold for both needle types (Factor B). There is no detectable difference in mean rope pull between the two needle types.

15. ReplicationWhy? It Allows You To See Things More Clearly! • Below Are the Normal Probability Plots for the First and Second Replication • Notice How Hard it is to see that the AC Interaction is Significant

16. ReplicationWhy? It Allows You To See Things More Clearly! • Below Are the Normal Probability Plots for the Individual Replicates and the one based on the Averages • Notice How Replication Makes it Easier to see that the AC Interaction is Significant

17. ReplicationWhy? It Allows You To Use ANOVA! • The Small p-Values in the ANOVA Table Indicate that there are Significant Main Effects and that the Interaction is Significant • The zero p-values in the Factor Effects Table Indicate that both A and B are Significant