Webinars
Stat-Ease offers free webinars that provide valuable advice on design of experiments (DOE) made easy and powerful via our statistical software. Topics range from beginner to advanced levels. Material may be new or drawn from our ever-popular DOE workshops. Register for upcoming live presentations below, or feel free to watch any of the wealth of previously recorded webinars.
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"Thank you very much for the continuing education opportunities through Stat-Ease's webinar offerings. The instructors do an excellent job of explaining DOE. Thank you, Stat-Ease!"
—Jeff Reimer, Principal Scientist, Sigma-Aldrich Corporation
Upcoming Live Webinars:
Via a series of case studies, this webinar demonstrates multicomponent and multifactor design-of-experiment (DOE) tools for optimal formulation and processing of foods. See how these tried-and-true statistical methods, made easy by Stat-Ease software, empower experimenters to quickly converge on the “sweet” spot—ingredient and factor settings that meet all specifications at minimal cost.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, June 1, 2022
Time: 10:00am Central US time
Discover what design of experiments (DOE) can do for you when catalyzed with DX13’s world-class statistical tools. Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM) for process optimization and last, but not least, a look into mixture design for optimal formulation. Whether you want to do more with Design-Expert as a current user, or need a better tool for doing DOE, or enlightenment as to what these powerful multifactor-testing methods can do for you, this free webinar will be of great benefit.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, June 8, 2022
Time: 10:00am Central US Time
Aprimore seus conhecimentos em Planejamento Experimental (Design of Experiments, DOE) por meio deste webinar que abordará os fundamentos desta ferramenta multivariada essencial. Esta explicação rápida irá mostrar porque o Planejamento de Experimentos (DOE) é uma ferramenta estatística tão eficaz para acelerar sua pesquisa e desenvolvimento. Descubra como é possível identificar fatores importantes e possíveis interações.
Não pode participar desta vez? Faça sua inscrição e voce será notificado quando a gravação estiver pronta.
Data: Quinta-feira, 23/06/2022
Horário: 12:00h Horário de Brasília
Recorded Webinars:
Building up from the Mixture DOE Crash Course, this webinar explains how formulators can create experiment designs that combine mixture components with process factors, include categorical factors, and deal with hard-to-change variables.
Step up your design of experiments (DOE) know-how via this essential briefing on this multifactor-testing tool. A quick demo lays out what makes statistical DOE so effective for accelerating R&D. Discover how DOE will find your vital few factors and reveal breakthrough interactions.
The multifactor tools of design of experiments (DOE), though proven for manufacturing quality improvement, remain underutilized in service, business administration and other transactional processes. See how DOE can be applied in these domains.
Discover what design of experiments (DOE) can do for you when catalyzed with DX13’s world-class statistical tools. Learn about factorial design, followed by a peek at response surface methods (RSM) for process optimization and lastly, a look into mixture design for optimal formulation.
This webinar provides valuable insights on Stat-Ease® 360 software’s special modeling tools for binary data, counts, and deterministic results (such as those collected from computer simulations). The focus will be on the practical aspects, with minimal emphasis on theory and technical details.
Before embarking on expensive experiments, it often pays to mine existing data. It may be gold, or it may be garbage, but why not try? This webinar demonstrates how easily Stat-Ease software imports results so you can then apply its powerful tools for evaluation, analysis and optimization.
Advance your R&D experimentation skills via this essential webinar on mixture experiments. A compelling demo lays out what makes mixture design of experiments (DOE) so effective for accelerating your formulation efforts. Discover how to: identify key characteristics leading to a mixture experiment, use mixture DOE to create optimal formulations, and map out your sweet spot with graphical tools.
Motivated by frequently asked questions from graduate researchers, this webinar lays out essential elements for good design of experiments (DOE).
Learn how power for factorial designs and precision for RSM and mixture designs can be used to properly size your DOE's to best achieve your objectives.
In many cases, experimental data is the result of a deterministic simulation rather than a lab experiment. These may be referred to as computer experiments. Such situations need special experimental designs and data analysis tools. See how Stat-Ease 360 fills this need with via space-filling designs and Gaussian process models.
See how multifactor testing tools are useful for elastomers, rubbers and composites R&D.
Learn how Python has been integrated into Stat-Ease 360. This tutorial walks through connecting Python, extracting data from SE360, and some other more complex examples.
This case study illustrates using a KCV mixture/process design to characterize and optimize a mixture system and the impact of dosage to handsheets. Wrap up with discussion of growing a DOE culture within a diverse organization.
A response surface design is used to gain process understanding of an IVF cell culture system.
This case study illustrates the use of candidate sets of data to build a custom design.
This talk features four examples making use of Design-Expert’s comprehensive design-building facilities to build the desired design while not revealing everything to DX.
Logistic regression provides a meaningful analysis for this mixture DOE on a metalworking fluid emulsion.
An I-optimal split-plot design is used in a wind tunnel aerodynamic performance characterization study.
Stat-Ease 360 augments Design-Expert's powerful DOE capabilites with Python scripting integration and tools for computer experiments. Learn about the latest innovations from Stat-Ease as well as plans for the future.
Learn the differing impacts of running repeated samples or measures, versus replicating runs. Knowledge of the sources of variation in the system and the costs of replicating the DOE run and/or repeating the measure can help one decide which is the best option.
Discover DOE tools aimed at developing systems that hold up when transferred to the field. It features factorials geared for testing many variables in a minimum number of runs—just enough to reveal effects that may lead to failure.
Design of experiments (DOE) is a tried-and-true, multifactor quality tool for identifying key process drivers. This webinar demonstrates how to deploy DOE to create reliable prediction models. Similar in concept to estimating the power of a design, prediction precision becomes the key evaluation statistic. A case study demonstrates how to confirm that a particular design will provide the desired results - more reliable process settings.
Via a series of case studies, this webinar demonstrates multifactor testing tools for aerospace R&D. See how Design-Expert empowers experimenters to quickly converge on the “sweet” spot—factor settings that meet all specifications.
Pat Whitcomb, Stat-Ease founder, illustrates how to take best advantage of designs geared for hard-to-change process settings. While running through a number of case studies with Design-Expert® software, he provides statistical details and practical advice on the pluses and minuses created by the split-plot factor layout.
Via a series of case studies illustrating Design-Expert® software’s new Poisson regression tool, Engineering Consultant Mark Anderson provides practical aspects for modeling counts; e.g., manufacturing defects. He will contrast and compare Poisson regression with ordinary least square regression (with and without a transformation).
Version 13 of Design-Expert software provides major new tools and wizards that make experimentation more effective and easier than ever. Learn about:
- Modify Design Space Wizard
- Poisson Regression
- Round Columns Tool
- Multiple Analyses
- Import Data Wizard
Optimize your products and processes with accurate prediction models. Learn how to get the most out of your RSM design by following a few key analysis steps. See how automated model-reduction tools build simpler models that predict more precisely. Then discover how diagnostics confirm your model’s validity. Finally, learn how key statistics like lack of fit and various R-squared measures characterize the polynomial model.
Response surface methods (RSM) provide a quick path to the peak of process performance. This webinar presents an array of RSM designs to choose from – central composite, Box-Behnken and optimal (custom). Learn when each design excels. Also find out how to handle categoric factors, discrete numeric levels and complex constraints involving multiple factors. Discover how to set up the right RSM design for your unique experimental needs.
In this advanced-level webinar, Stat-Ease Consultant Pat Whitcomb discusses 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.
This talk deals with thorny issues that confront every experimenter: How to handle results that fit badly with your chosen model. Design-Expert software provides graphical tools that make it easy to diagnose what is wrong—damaging outliers and/or a need for transformation. A variety of case studies will demonstrate the value of these diagnostics.
Discover the secrets to customizing your experiments using optimal (custom) designs. Learn the importance of adding lack of fit points and replicates. All these issues are considered at a practical level – keeping the actual experimenters in mind.
Rollback the covers on the incredibly useful optimization tools provided by Design-Expert® software (DX). Discover how DX manipulates multiple response-models to search out the most-desirable sweet spot. Master the controls for setting goals, changing relative importance, and many other options that lead to an optimal outcome.
DOE is often presented as a “one shot” approach. It may be more efficient to divide the experiment into smaller pieces, thus expending resources in a more adaptive manner. This sequential approach becomes especially suitable when beginning with very little information about the process, for example, when scaling up a new product. It allows for better definition of the design space, adaption to unexpected results, estimation of variability, reduction in waste, and validation of the results.
A nice new addition to Design Expert is the KCV designs (Kowalski, Cornell, and Vining 2000 and 2002) for experiments that involve both mixture components and process variables. This talk presents an overview on these designs. It begins with a brief history of their origin. It then motivates the basic approach for the construction of these designs and contrasts this approach to other approaches popular at that time. It then discusses some of the subtleties involved in analyzing these designs. An example illustrates their use.
In today’s Industry 4.0, industrial processes are becoming increasingly complex, presenting significant challenges to the industrial experimenter. In particular, modern experimental design practice can often lead to non-standard situations. In this talk I will discuss some examples of the non-standard experimental design situations I’ve encountered in modern practice, with the common denominator in all these situations being a split-plot treatment structure.
By way of example, this presentation lays out a strategy for design of experiments (DOE) that provides maximum efficiency and effectiveness for development of a robust process. It provides a sure path for converging on the ‘sweet spot’—the most desirable combination of process parameters and product attributes. Whether you are new or experienced at doing DOE, this talk is for you (and your organization's bottom line!).
Pat Whitcomb details the cost-saving mixture-process models developed by Scott Kowalski, John Cornell and Geoff Vining (KCV). Design-Expert® software, version 12, drops this modeling tool right into the user's hands. See how it reduces the number of model terms and thereby reduces the number of runs required to estimate the complex relationship between mixture and process variables. Estimated length: 45 min.
Martin Bezener, Stat-Ease Consultant, introduces Design-Expert v12’s new tools for logistic regression for data that is binary, like pass/fail.
Discover how to optimize your process while avoiding impossible factor combinations.
This case-study driven webinar is a must for all who experiment on APIs. Learn how to apply statistically valid, multifactor and multicomponent testing strategies that catalyze your development work.
Use mixture design tools for multi-component product development and optimization, perfect for formulators. Learn why factorial designs won't work.
Pat Whitcomb reveals some tricks for making the most of your DOE.
- Using std error to constrain optimization
- Using Cpk to optimize your DOE
- Combining categoric factors
- Using diagnostics to uncover analysis problems
Use graphical tools (half-normal & Pareto plots) to select effects quickly and accurately.
How to use automatic model selection tools to build on appropriate models. Pros and cons of the methods are discussed.
- Forward/Backward/Stepwise
- p-value, AICc, BIC, All Subsets
Learn how multivariate analysis (MVA) methods can be used in combination with design of experiments (DOE) tools. Presenters demonstrate how to build an optimal design from principal components and use the DOE results to find an optimal compound.
Review strategies for running confirmation to verify the results of an experiment.
Topics include foldovers, semifoldovers, building a CCD from a one-factor-at-a-time (OFAT) study, and optimal augmentation for RSM designs.
A briefing on QbD, along with state-of-the-art response surface methods (RSMs) for developing a robust design space.
Historical data can be difficult to analyze - look out for these common issues.
Verification is an essential final step to improving a product or process. Learn how DOE makes this a breeze.
A dual response approach to response surface methods (RSM) provides a statistically sound way to make your process more robust.