An introduction to SigmaXL's Design of Experiments tools. Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation. SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies. Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.  DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes. DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.

1. Design of Experiments SigmaXL® Version 6.1
DOE Overview
Basic DOE Templates
2-Level Factorial and
Plackett-Burman Screening
Designs
Example: 3-Factor, 2-Level
Full-Factorial Catapult DOE
Main Effects & Interactions Plots
Analyze 2-Level Factorial and
Plackett-Burman Screening Designs
Analyze Catapult DOE
Predicted Response
Calculator
Response Surface Designs
Contour & 3D Surface Plots

2. Design of Experiments

Basic DOE Templates





Automatic update to Pareto of Coefficients
Easy to use, ideal for training
Generate 2-Level Factorial and PlackettBurman Screening Designs
Main Effects & Interaction Plots
Analyze 2-Level Factorial and PlackettBurman Screening Designs
Back to
Index

3. Basic DOE Templates
Back to
Index

4. Design of Experiments:
Generate 2-Level Factorial
and Plackett-Burman
Screening Designs





User-friendly dialog box
2 to 19 Factors
4,8,12,16,20 Runs
Unique “view power analysis as you design”
Randomization, Replication, Blocking and
Center Points
Back to
Index

5. Design of Experiments:
Generate 2-Level Factorial
and Plackett-Burman
Screening Designs
View Power Information
as you design!
Back to
Index

6. Design of Experiments
Example: 3-Factor, 2-Level
Full-Factorial Catapult DOE
Objective: Hit a target at exactly 100 inches!
Back to
Index

7. Design of Experiments:
Main Effects and
Interaction Plots
Back to
Index

8. Design of Experiments:
Analyze 2-Level Factorial
and Plackett-Burman
Screening Designs




Used in conjunction with Recall Last Dialog, it
is very easy to iteratively remove terms from
the model
Interactive Predicted Response Calculator
with 95% Confidence Interval and 95%
Prediction Interval.
ANOVA report for Blocks, Pure Error, Lack-offit and Curvature
Collinearity Variance Inflation Factor (VIF)
and Tolerance report
Back to
Index

9. Design of Experiments:
Analyze 2-Level Factorial
and Plackett-Burman
Screening Designs




Residual plots: histogram, normal probability
plot, residuals vs. time, residuals vs. predicted
and residuals vs. X factors
Residual types include Regular,
Standardized, Studentized (Deleted t) and
Cook's Distance (Influence), Leverage and
DFITS
Highlight of significant outliers in residuals
Durbin-Watson Test for Autocorrelation in
Residuals with p-value
Back to
Index

10. Design of Experiments
Example: Analyze Catapult
DOE
Pareto Chart of Coefficients for Distance
25
Abs(Coefficient)
20
15
10
5
B:
BC
AB
AC
AB
C
St
op
Pi
n
gh
t
He
i
C:
Pi
n
A:
Pu
ll B
ac
k
0
Back to
Index

11. Design of Experiments:
Predicted Response
Calculator
Excel’s Solver is used with the
Predicted Response Calculator to
determine optimal X factor
settings to hit a target distance of
100 inches.
95% Confidence Interval and
Prediction Interval
Back to
Index

12. Design of Experiments:
Response Surface Designs



2 to 5 Factors
Central Composite and Box-Behnken Designs
Easy to use design selection sorted by number of
runs:
Back to
Index

13. Design of Experiments:
Contour & 3D Surface Plots
Back to
Index