Webinars
Stat-Ease offers free webinars that provide valuable advice on design of experiments (DOE) made easy and powerful via our statistical software. Register for upcoming live presentations below.
Want more? Discover our comprehensive set of educational webinars on the Stat-Ease YouTube channel!
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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:
Discover what design of experiments (DOE) can do for you when catalyzed with Design-Expert’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.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, June 6, 2022
Time: 10:00am Central US Time
This webinar details incredibly useful assessments provided by Stat-Ease software for evaluation of any set of input data, whether existing (unplanned) or from a ‘proper’ design of experiments (DOE). Learn how to watch for issues that degrade the information that you hope to extract and strengthen your ability to assess your data quality!
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, June 14, 2023
Time: 10:00am Central US Time
Erweitern Sie Ihr Know-How zur statistischen Versuchsplanung (DoE) mit diesem Grundlagenseminar zur Untersuchung und Optimierung multipler Faktoren. Anhand einer kurzen Demonstration erläutern wir die Vorteile statistischer Versuchsplanung im Forschungs- und Entwicklungsumfeld.
Der Termin passt Ihnen nicht? Registrieren Sie sich dennoch, Sie werden benachrichtigt, sobald eine Aufzeichnung verfügbar ist.
Termin: Dienstag, der 20. Juni 2023
Uhrzeit: 14 Uhr MESZ
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.
See how multicomponent and multifactor design-of-experiment (DOE) tools empower experimenters to quickly converge on the “sweet” spot—ingredient and factor settings that meet all specifications at minimal cost. All examples come directly from biotech industries.
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.
Gleaned from 30 years of running and analyzing designed experiments, these are the things that ultimately lead to great learning opportunities from DOE’s, versus dismal failures with wasted time and effort. Novices to experimentation will benefit from this insightful presentation!
Discover what design of experiments (DOE) can do for you when catalyzed with Design-Expert’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.
If done properly, design of experiments (DOE) provides huge process improvements via small screening studies. Unfortunately, many experimenters deploy designs such as Plackett-Burmans (PBs) that cannot resolve main effects from potential interactions—these being confounded (aliased). This webinar will evaluate more suitable designs for reliable screening at a minimum number of experimental runs.
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. In other cases, physical experiments may produce low or zero-error response measurements. 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.
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.
Discover methods for creating experiment designs progressively so that knowledge can be gained steadily via iterative steps. Learn how to augment completed designs that fall short of adequately modeling the critical response(s). This might salvage a great deal of experimental work that would otherwise go for naught.
Renforcez votre savoir-faire sur les Plans d’Expériences (DOE) grâce à ce webinaire sur cet outil de test multifactoriel. Une démonstration rapide vous expliquera pourquoi les DOE sont si efficaces pour booster votre R&D et vous aider à approfondir vos procédés. Découvrez comment les DOE permettent d’identifier vos facteurs critiques et de mettre en lumière les interactions essentielles.
By way of example, this presentation lays out a strategy for mixture design of experiments (DOE) that provides maximum efficiency and effectiveness for development of an ideal product recipe. It provides a sure path for converging on the ‘sweet spot’—the most desirable combination of components. Learn how to screen down many ingredients to find the vital few and then discover their optimal formulation.
After decades of continuous development, Design-Expert® software (DX) leads the field for making design of experiments (DOE) easy. In response to many requests from loyal users, we are proud to now produce Stat-Ease® 360 (SE360). This webinar provides a briefing on the major innovations now available with SE360, and bit of what's in store for the future.
Save time and costs by utilizing smaller designs! In this webinar Stat-Ease consultant, Shari Kraber, reveals the information provided by both regular-fraction versus more-modern minimum-run designs—a Stat-Ease invention. Take away a clear guide for selecting the best design based on your factorial DOE objective: screening or characterization.
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.
In this talk, Mark Anderson details cost-saving mixture-process methods invented by statisticians Kowalski, Cornell and Vining (KCV) and implemented by Stat-Ease. The KCV tools streamline combined designs by focusing on the interactions—the hidden gold remaining buried by traditional experimentation. Via a real-world example, Mark will present experiment-design and modeling methods that make combined mixture-process studies practical for chemists.
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.
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.
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.
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.
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.
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.
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.
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
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