2016 Stat-Ease, Inc.

Transcription

1 What s New in Design-Expert version 10 Presentation is posted at There are many attendees today! To avoid disrupting the Voice over Internet Protocol (VoIP) system, I will mute all. Please use the Questions feature on GotoWebinar which we will answer during the presentation. -- Pat Presented by Pat Whitcomb, Founder Stat-Ease, Inc., Minneapolis, MN pat@statease.com April 2016 Webinar 1 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 2 Copyright 2016 Stat-Ease, Inc. Do not copy or redistribute in any form. 1

2 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 3 User Interface Design Wizard provides guidance for less sophisticated users Expanded Undo/Redo option in Design Layout Column sort from View menu for Design Layout Run column Reorder as currently displayed option for split plots Smoother scrolling of reports and spreadsheets Support for different decimal point characters (localization) LOESS Tool for Fit line in Graph Columns Enhanced constraint tool with simplified equations and clear button Splash screen upon start (replaces About Box) Better handling of progress bars including threading April 2016 Webinar 4 copy or redistribute in any form. 2

3 Design Wizard April 2016 Webinar 5 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 6 copy or redistribute in any form. 3

4 Design Build Design Build: Central Composite and Optimal split-plots for Response Surface designs Optimal split-plots for Combined (mixture-process & mix-mix) designs Variance ratio display on the power response-entry screen for factorial split-plots Blocking for Definitive Screening Designs (DSDs) Historical data choice for Combined designs Power and sample size calculator for binomial responses during build April 2016 Webinar 7 CCD Split-Plots for Response Surface April 2016 Webinar 8 copy or redistribute in any form. 4

5 CCD Split Plot Airfoil configuration DOE 1 Wind tunnel experiment: Two HTC factors: Offset Gap Deflection angle Two ETC factors: Wing Gap Deflection Angle of attack Reynolds number 1. Loosely based on: Tutorial: Industrial Split-plot Experiments, Kowalski, S.M., Parker, P. P. and Vining, G. G., Quality Engineering, 19: 1-15, April 2016 Webinar 9 CCD Split Plot Build the Design (page 1 of 2) 1. Go to the Response Surface tab and choose a Split-Plot, Central Composite design for four factors. 2. Enter the factor names and make the first two factors HTC: April 2016 Webinar 10 copy or redistribute in any form. 5

6 CCD Split Plot Build the Design (page 2 of 2) 3. Click on the Options button and change the Alpha value to Practical : Click: OK and Continue>> 4. Enter the response name: Lift and click on Finish. April 2016 Webinar 11 CCD Split Plot Twelve Groups (whole plots) April 2016 Webinar 12 copy or redistribute in any form. 6

7 Corn Milling Background 1 (page 1 of 2) This experiment involves milling corn. The goal is to model the effect of four treatment factors on the amount of grits that can be obtained from a one-minute run of a grinding mill. The four factors are: 1) moisture content of the corn (a), 2) roll gap (B), 3) screen size (C), 4) roller speed (D). April 2016 Webinar 13 Corn Milling Background (page 2 of 2) To prepare corn for the experiment, a batch of corn (30 kg) has to be tempered to the desired moisture content. Thus it was decided to prepare a batch of corn to satisfy a specified moisture content, split the batch of corn into three parts (10 kg each), and then carry out three runs involving the other three factors. An RSM optimal split-plot design is selected with four factors, each at three levels, with 30 runs. The runs were grouped into sets of three, where each set of three had the same level of moisture. The order of the three runs within a group is randomized and the order of the sets of three runs is randomized. April 2016 Webinar 14 copy or redistribute in any form. 7

8 Corn Milling Optimal Three-Level Split Plot (page 1 of 2) 1. Go to the Response Surface tab and choose a Split-Plot, Optimal (custom) design for four factors. 2. Enter the factor names, make the first factor HTC and make all the factor types Discrete with 3 levels: April 2016 Webinar 15 Corn Milling Optimal Three-Level Split Plot (page 2 of 2) 3. Enter 7 Additional groups and 15 Additional model points : 4. Enter one response Yield : April 2016 Webinar 16 copy or redistribute in any form. 8

9 Split-Plot Designs In combined designs often either the process factors or the mixture components are hard to change (HTC). Process Factors HTC Mixture Components HTC April 2016 Webinar 17 Reverse Phase HPLC Two Process Factors Design considerations: The mobile phase is easy to mix and change. The column factors (particle and pore sizes) are difficult and time consuming to change. The experiments will be easier, faster and less costly if the mixture components are specified as sub-plot treatments and the frying conditions are whole-plot treatments. Use a split-plot combined design with Mix components as ETC and Numeric factors as HTC. April 2016 Webinar 18 copy or redistribute in any form. 9

10 Sweet Potato Chips Background Design considerations: The soaker is large and handles lots of potato slices. The fryers are small and it is easy to change frying conditions. The experiments will be easier, faster and less costly if the mixture components are specified as whole-plot treatments and the frying conditions are sub-plot treatments. Use a split-plot combined design with Mix components as HTC and Numeric factors as ETC. April 2016 Webinar 19 Power and Sample Size Calculator for Binomial Responses during Build 2 4 Full Factorial April 2016 Webinar 20 copy or redistribute in any form. 10

11 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 21 Model Selection Automatic Model Selection tool for choosing Criterion and Selection method AICc, BIC and Adjusted R-squared criteria in algorithmic selection Select by Degree option prioritizes the criteria comparisons by term order Model Selection Log in Automatic Model Selection and in ANOVA Likelihood ratio p-values for split-plot designs Definitive Screening Designs (DSD) moved to Response Surface tab and analyzed as a supersaturated matrix for quadratic modeling Aliases reported and selectable in Effects List (makes Alias List unnecessary) April 2016 Webinar 22 copy or redistribute in any form. 11

12 Automatic Model Selection Tool (page 1 of 3) April 2016 Webinar 23 Automatic Model Selection Tool (page 2 of 3) Design-Expert software offers four Criterion and several Selection methods. Criterion AICc BIC p-values Adj R-Squared Selection method Forward & Backward Forward & Backward Forward, Backward & Stepwise All Hierarchical For experiments with minimal collinearity all combinations of criterion and selection method work well. However, constraints and other design abnormalities can make it harder to determine the best model. Therefore we recommend using p-values and backward elimination, in addition to the default of AICc and forward selection. Compare their respective models, if they agree then you re done. If not, then try other methods before settling on a model. All model reduction must be guided by the experimenter's subject matter knowledge! April 2016 Webinar 24 copy or redistribute in any form. 12

13 Automatic Model Selection Tool (page 3 of 3) Automatic Model Selection is used to algorithmically choose the terms to keep in the model. The Criterion is the statistic used to make the decision for how to choose the best model. There are four choices: AICc is the default criterion. AICc stands for Akaike's (pronounced ah-kah-ee-kay) Information Criterion with a correction for a small design. When comparing the AICc values for two models, the model with the smaller value is chosen. BIC is an alternate to AICc that performs better for larger designs. BIC stands for Bayesian Information Criterion. When comparing the BIC values for two models, the model with the smaller value is chosen. In general, BIC penalizes models with more parameters more than AICc does. For this reason it generally leads to models with fewer parameters than the AICc criterion. p-value is the traditional method looking for statistically significant terms to keep and/or insignificant terms to remove from the model. Adjusted R-Squared is the fourth criterion. Only All Hierarchical Subsets selection is used with Adjusted R-Squared. This criterion may have a time-consuming search for a large set of terms. Automatic Model Selection is not intended to replace the analyst's decisions. Please take the time to review the results on the ANOVA and Diagnostics before using the model to make decisions. April 2016 Webinar 25 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 26 copy or redistribute in any form. 13

14 Analysis Option to ignore groups for split-plot designs Split-plots analyzed using either Maximum Likelihood (ML) or REML Equations section on the split-plot ANOVA Tolerance Intervals for predictions in split-plot designs VIFs in split-plot ANOVA Adjustable REML/ML stopping rule and maximum iterations Negative variances excluded when calculating REML/ML Variance components in mixed-models zeroed out below a threshold Exact computation of R 2 in split-plot designs Block variance no longer included when calculating LSD values Option to choose between one-sided and two-sided tests for intervals New View item to see the V-matrix used in Mixed Model calculations April 2016 Webinar 27 Maximum Likelihood (ML) or REML April 2016 Webinar 28 copy or redistribute in any form. 14

15 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 29 Diagnostics Box-Cox plot for mixed models, e.g., split plots Externally studentized residuals and influence graphs for mixed models (Cook's Distance, DFFITS, Covariance Trace and Covariance Ratio) Control limit defaults values for DFFITs and DFBETAs with option not to display on graph Limit lines on diagnostic graphs labeled Color by Group in the Residual vs Factor diagnostic graph Footnote to Diagnostics report indicates why deletion statistics not displayed April 2016 Webinar 30 copy or redistribute in any form. 15

16 Corn Milling An Outlier? (page 1 of 6) Run #16 appears discrepant with the rest of the data: Normal Plot of Residuals Residuals vs. Predicted Predicted vs. Actual Normal % Probability Externally Studentized Residuals Predicted Externally Studentized Residuals Predicted Actual April 2016 Webinar 31 Corn Milling An Outlier? (page 2 of 6) Run #16 doesn t influence the fixed effects (the model coefficients) very much: Cook's Distance Fixed Leverage vs. Run DFFITS vs. Run Cook's Distance Fixed Leverage DFFITS Run Number Run Number Run Number April 2016 Webinar 32 copy or redistribute in any form. 16

17 Corn Milling An Outlier? (page 3 of 6) Run #16 doesn t influence the model much: With run #16 without run #16 Yield = Yield = * a * a * B * B * C * C * D * D * ab * ab * BD * BD * CD * CD * a 2 April 2016 Webinar 33 Corn Milling An Outlier? (page 4 of 6) Run #16 seems to influence the random effects (the coefficient standard errors) a lot: Cook's Distance Random Covariance Ratio Covariance Trace Cook's Distance Random Covariance Ratio Covariance Trace Run Number Run Number Run Number April 2016 Webinar 34 copy or redistribute in any form. 17

18 Corn Milling An Outlier? (page 5 of 6) with run #16 without run #16 Coefficient Standard Source Estimate Error Intercept a-moisture B-roll gap C-screen size D-roller speed ab BD CD Coefficient Standard Source Estimate Error Intercept a-moisture a^ B-roll gap C-screen size D-roller speed ab BD CD April 2016 Webinar 35 Corn Milling An Outlier? (page 6 of 6) In this exercise including run # 16 primarily increases the residual variance while having little effect on the model coefficients: with run # 16 without run # 16 Variance Components Variance Components Source Variance Source Variance Group Group Residual 6.99 Residual 1.39 Total 6.99 Total 1.39 April 2016 Webinar 36 copy or redistribute in any form. 18

19 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 37 Graphs All Factors view (factor profiler) on model graphs All Reponses choice in Numerical Optimization Interactive LSD (least significant difference) bars Improved flexibility in sizing and placement of flags on graphs Show ignored values on Predicted vs Actual graphs View menu item to turn toggle on/off the factor names on Effect plots Useful version 9 & 10 item I didn t know about: Can right click to add a comment to a point (run) on the diagnostic graphs April 2016 Webinar 38 copy or redistribute in any form. 19

20 All Factors April 2016 Webinar 39 All Reponses April 2016 Webinar 40 copy or redistribute in any form. 20

21 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 41 Computation & Evaluation Computational Capability 64-bit version Math engine retooled for far-faster computations Optimal builds now run in parallel making them 3 to 17 times faster Designs no longer limited to 32K runs Design Evaluation: Expanded Evaluation Report for split-plot designs View menu item to see the V-matrix used in mixed model calculations Power and sample size calculator for binomial response April 2016 Webinar 42 copy or redistribute in any form. 21

22 Power and Sample Size Calculator for Binomial Response 2 4 Full Factorial April 2016 Webinar 43 Agenda User Interface Design Build Model Selection Analysis Diagnostics Graphs Computational Capability Design Evaluation Highlights: Recap April 2016 Webinar 44 copy or redistribute in any form. 22

23 Design-Expert version 10 Highlights: Recap (page 1 of 2) Design Wizard provides guidance for less sophisticated users Multiple graphs view (factor profiler) All Reponses choice in Numerical Optimization Blocking for Definitive Screening Designs Add Historical data to combined design AICc/BIC algorithmic selection Binomial power calculation during factorial build 64 bit version with improved multi-core support Run optimal builds in parallel to speed up processing greatly Support for different decimal point characters (localization) April 2016 Webinar 45 Design-Expert version 10 Highlights: Recap (page 2 of 2) Optimal split plots for RSM CCD split plots Optimal split plots for Combined designs Half-normal plots for all 2-level split-plots Split plots analyzed using either REML or ML Forward selection for REML/ML analysis Equations section on the split-plot ANOVA Improved split-plot diagnostics (e.g. REML Box-Cox) Ability to ignore groups for split-plot designs Show V matrix in design evaluation April 2016 Webinar 46 copy or redistribute in any form. 23

24 Reminder, this presentation is posted at: If you have additional questions them to: Thank you for joining us today! April 2016 Webinar 47 copy or redistribute in any form. 24