Stat-Ease is proud to offer free webinars to those interested in Design of Experiments (DOE). Presented on a regular basis, topics range from beginner to advanced levels. Material may be new or drawn from our ever-popular DOE workshops.
See the list of upcoming and past webinars below. If you attended one of our presentations (or just wish you had) we invite you to download a copy of it for your review.
Stat-Ease is your resource for all things DOE. If there is a particular subject you are interested in and don't see below, send us an e-mail and let us know. If there is enough interest, we may present your topic in a future webinar.
Thank you for visiting Stat-Ease. We appreciate your interest!
"Quality by Design (QbD) Space for Pharmaceuticals and Beyond" Intermediate Level
Quality by Design (QbD) is a hot topic in the pharmaceutical industry, heavily promoted by the FDA. However, these tools should be used by every industry interested in producing high-quality products. The general concepts are not new, but the tools to implement them have dramatically improved in the last few years. This presentation provides a briefing on QbD along with state-of-the-art response surface methods (RSMs) for developing a robust design space.
Register for the session you are interested in by clicking on the link below:
2. Thursday, June 27th, 2013 at 6:30 am USA-CDT (for Europe and India)
3. Tuesday, July 2nd, 2013 at 8:00 pm USA-CDT (for Asia and Australia)
Stat-Ease webinars vary somewhat in length depending on the presenter and the particular session—mainly due to breaks for questions: Plan for 45 minutes to 1.5 hours, with 1 hour being the target median. When developing these one-hour educational sessions, our presenters often draw valuable material from Stat-Ease DOE workshops.
Attendance may be limited for these webinar sessions, which run from 45 minutes to 1.5 hours, with 1 hour being the norm. To sign up for the webinar, click on the corresponding link above the presenter's picture. Contact our Communications Specialist, Karen Dulski with any questions.
Please note: We recommend you test your computer in advance. Click here for details.Testimonial:
"Thank you very much for the continuing education opportunities through Stat-Ease's webinar offerings. Brooks did a fine job yesterday presenting RSM Part 2. I've also listened to past webinars presented by other instructors and they also do an excellent job of explaining DOE. Thank you, Stat-Ease!"
—Jeff Reimer, Principal Scientist, Sigma-Aldrich Corporation
"How to Get Started with Design-Expert® Software" Beginner Level (If you missed this webinar, just click on the link to the left to view a recording of it at your convenience. For a PDF of the slides, click here.)
Geared toward novices to design of experiments (DOE) and Design-Expert software, this presentation demonstrates how to plan, design and analyze a powerful multifactor test. The speaker also provides a ‘heads-up’ on mistakes made by unsuspecting beginners that lead to DOE failures. The goal of this webinar is to set you up for success using Stat-Ease software for your experiments!
"Real-Life DOE" Beginner Level (If you missed this webinar, just click on the link to the left to view a recording of it at your convenience. For a PDF of the slides, click here.)
Mark Anderson, principal of Stat-Ease, Inc.(Minneapolis, Minnesota, USA) and co-author of the DOE/RSM Simplified textbook series (Productivity, Inc., New York, NY) provides some tricks of the trade that salvaged outstanding results from actual experiments. If you are just beginning with factorial screening and characterization experiments, this webinar is for you! More advanced practitioners might also glean an “aha!” as well, and/or follow up afterward with suggestions that Mark can share via his DOE FAQ Alert e-mail newsletter.
"Overview of Robust Design, Propagation of Error, and Tolerance Analysis" Advanced Level (If you missed this webinar, just click on the link above to view a recording of it at your convenience. For a PDF of the slides only, click here.)
Response Surface Methods (RSM) can lead you to the peak of process performance. In this advanced-level webinar, Stat-Ease Consultant Pat Whitcomb will discuss robust design, propagation of error, and tolerance analysis. Propagation of error (POE) accounts for variation transmitted from deviations in factor levels. It finds the flats—high plateaus or broad valleys of response, whichever direction one wants to go—maximum or minimum; respectively. Tolerance analysis drills down to the variation of individual units, thus facilitating improvement of process capability.
"Basics of Response Surface Methodology (RSM) for Process Optimization, Part 2" Intermediate Level (If you missed this webinar, just click on the link above to view a recording of it at your convenience.) For a PDF of the slides only, click here.)
"An Introduction to Design-Expert Software for Design of Experiments (DOE)" (If you missed this webinar, just click on the link above to view a recording of it at your convenience.) For a PDF of the slides only, click here.)
Pat Whitcomb and Shari Kraber introduce tools for multifactor process improvement, product development and optimization. Three case studies are presented, illustrating the use of factorial design, response surface design, and mixture design. These designed experiments are shown, along with their ties to AAO software for Beckman Coulter Biomek FX equipment.
"Basics of Response Surface Methodology (RSM) for Process Optimization, Part 1" (If you missed this webinar, just click on the link above to view a recording of it at your convenience. For a PDF of the slides only, click here.) Intermediate Level
Encore presentation was presented on Tuesday, October 18th, 2011 at 10:30 am. Was 1st presented on Thursday, September 8th, 2011 at 2 pm.
Response Surface Methods (RSM) can lead you to the peak of process performance. In this webinar, Shari Kraber introduces the fundamental concepts of response surface methods (RSM). You will look at the central composite design and learn about multiple response optimization while working through an actual case study application.
"Practical Aspects of Algorithmic Design of Physical Experiments" (Click on the title at left to download, 6.83 MB) Intermediate Level
"How to Get Started with DOE" (Click on the title at left to download, 843 KB) Beginner Level
Stat-Ease Consultant Brooks Henderson incorporated his Whirley-Pop DOE and some tips from the past webinars into this presentation. If you are new to DOE, this webinar is for you!
"DOE Made Easy and More Powerful via Design-Expert® Software, Part 3—Multicomponent Mixture Design for Optimal Formulation" (Click on the title at left to download, 1.50 MB) Advanced Level
"DOE Made Easy and More Powerful via Version 8 of Design-Expert® Software, Part 2—Response Surface Methods (RSM) for Process Optimization" (Click on the title at left to download, 1.13 MB) Advanced Level
Through a series of three webinars, Stat-Ease introduces an array of statistical methods for design of experiments (DOE) made easy and more powerful via version 8 of Design-Expert software. This second webinar looks at response surface methods (RSM) for process optimization through a series of case studies.
"DOE Made Easy and More Powerful via Version 8 of Design-Expert® Software" (Click on the title at left to download, 2.13 MB) Intermediate Level
Through a series of three webinars, Stat-Ease introduces an array of statistical methods for design of experiments (DOE) made easy and more powerful via version 8 of Design-Expert software. This first webinar highlights key features from simple to sublime, culminating in the design and analysis of a high-level factorial case-study.
"Problems Analyzing Historical Data" (Click on the title at left to download, 659 KB) Intermediate Level
"DOE—What's In It for Me " (Click on the title at left to download, 476 KB) Managerial Level
This webinar is aimed at those who are unclear or need convincing on how design of experiments (DOE) harnesses the power of matrix-based multifactor testing. Wayne will discuss and demonstrate the clear advantages of DOE over the old-fashioned one-factor-at-a-time (OFAT) method. Learn how the interactions that DOE reveals are the key to big success!
"An Introduction to Mixture Design for Optimal Formulations" (Click on the title at left to download, 760 KB) Beginner Level
This webinar is aimed at product formulators who at best may be using standard factorial designs, or worse yet, the one-variable-at-a-time method. Keeping it simple and making it fun, Mark introduces tools of multicomponent mixture design, modeling and statistical analysis. The goal is to generate interest in these powerful DOE methods for quickly converging on the sweet spot—where all desired product attributes are achieved.
"How to Plan and Analyze a Verification DOE" (Click on the title at left to download, 310 KB) Intermediate Level
Applications of DOE during Verification Stage
"Pat-Tricks on Model Diagnostics "What are They? Why Use Them? What Good Do They Do?” (Click on the title at left to download, 300 KB) Intermediate Level
In this webinar Pat Whitcomb (Consultant) will offer up his "Best Pat-Tricks on Model Diagnostics (What are they? Why use them? What good do they do?)." These questions were answered while examining the diagnostics for a series of DOE case studies. Download a ZIP file of the Design-Expert data files mentioned in the webinar here (pat_tricks_data.exe, 126 KB). For your reference, also take a look at the "Diagnostics Report—Formulas & Definitions" (click here to download, 48 KB) and the "Residual Analysis and Diagnostics Plots Guide" (click here to download,122 KB).
“Dual Response Surface Methods (RSM) to Make Processes More Robust” (Click on the title at left to download, 2.18 MB) Intermediate Level
Response surface methods (RSM) provide statistically-validated predictive models, sometimes referred to as "transfer functions," that can then be manipulated for finding optimal process configurations. The dual response approach to RSM captures both the average and standard deviation of the output(s) and simultaneously optimizes for the desired level at minimal variation, thus achieving an on-target, robust process. With inspiration provided by a case study on a semiconductor etching process, the positive repercussions of these methods will be readily apparent, especially for those involved in design for six sigma (DFSS) quality programs.
“The Difference Between Repeats and Replicates in DOE” Basic to Intermediate Level
In this presentation examples are used to illustrate the differences between replicates, duplicates, and repeats, as well as the reasons for using each. Cost-based decision selection of one versus another are discussed. This is a practical presentation with a dash of technical spice thrown in for flavor.
For a heads-up on this tricky issue, consider this advice from consultant Pat Whitcomb for FAQ #1 in the November, 2004 DOE FAQ Alert: "Another question might be can I repeat the measurement rather than replicate the DOE run? The answer is yes, but in this case you enter the average of the repeated measures, not the individual results. Independent measurements will reduce the measurement system component of the total process variation... Only with knowledge of the variance components and the costs of replicating the DOE run and/or repeating the measure can one decide which is the best option." (See http://www.statease.com/news/faqalert4-11.html for Pat's complete answer, including a sample calculation on variance components. Wayne's webinar addresses this and much more.)
Data files and PowerPoint presentation can be downloaded as the WinZip archive: 08-May-Webinar.zip
"Multiple Response Optimization with Design-Expert" (4.91 MB) Intermediate Level
The optimization module in Design-Expert searches for combinations of factor levels that simultaneously satisfy the requirements placed on each of the responses and factors. Discover how to get the most out of the optimization module in order to find the "sweet spot" for your product or an operating window for your process. Learn how to fine-tune your search by adding weights and importance settings to your basic criteria. A case study will be used to illustrate all of the features of Design-Expert's optimization module.
"10 Ways to Mess Up an Experiment & 8 Ways to Clean It Up" (1.04 MB) Basic Level
This basic presentation is intended for actual experimenters and applied statisticians who are looking for practical advice. It's all about design of experiments itself and how to do it more effectively.
Mark says, "Here's how this presentation came about. After decades in the trenches, primarily working on injection-molding process improvement, Stat-Ease's client, Jeff Hybarger, established his consultancy and wrote "'The Ten Most Common Designed Experiment Mistakes" as a white paper that documented his DOE 'chops.' Stat-Ease published the article in its Stat-Teaser newsletter. Design Product News picked it up in their June/July 2007 issue. The Fall Technical Conference of applied statisticians invited Jeff to talk about it. He bowed out due to scheduling conflicts so I edited and presented "The Ten Most Common Designed Experiment Mistakes."
For this webinar I summarized these 10 ways to mess up an experiment and recapped 8 ways to clean them up. This latter part stems from a talk developed by Consultant Shari Kraber with my collaboration. It was originally presented under the title of the "8 keys to DOE.""
"Sizing Mixture (RSM) Designs for Adequate Precision via Fraction of Design Space (FDS)" (1.04 MB) Advanced Level
We begin with a review of power calculations to determine if a factorial design has enough runs to detect effects. Power, however, is not the appropriate tool to evaluate mixture and response surface designs. This presentation shows how to use the fraction of design space (FDS) tool (only in DX7.1+) to properly size these more powerful designs. The use of FDS is also dependent on the experimenter’s design objectives—precision, prediction, or detecting a change. All three objectives are discussed.
"A Factorial Design Planning Process" (261 KB) Intermediate Level
This talk outlines a four-step process for planning a factorial design. A substantial part of this process is to evaluate the power of the design, which is based on detecting a specific change in the response versus the process variation present in the system. Via a case study, this talk addresses the issue of replicating runs versus repeating the measurement to increase the power of the design.
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