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.
Enjoy 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:
Learn how multicomponent and multifactor design-of-experiment (DOE) tools empower experimenters to quickly converge on the quality by design (QbD) “sweet” spot—ingredient and factor settings that meet all specifications at minimal cost. All examples come directly from pharmaceutical industries.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Friday, December 13, 2024
Time: 5:00 PM Indian Standard Time
Recorded Webinars:
This talk provides a briefing on design of experiments (DOE) for rapid and effective screening, characterization and optimization of factors affecting reliability. It covers a broad range of design of experiment (DOE) tools, including factorial design, Weibull regression, split plots for hard-to-change factors and response surface methods (RSM) for minimizing failure rates. Anyone tasked with improving reliability will benefit by learning how to make the most from every experiment via these multifactor testing methods.
This webinar upgrades robustness considerations previously discussed for characterization via factorial design to the optimization phase of process or product development. It lays out response surface methods (RSM) that identify regions within the design space that not only meet all response specifications, but also minimizes variation caused by wandering factor settings and extraneous noise factors. Attend this webinar to learn advanced RSM tools such as propagation of error (POE) to discover the robust high plateaus of performance.
This presentation makes the case for modeling both mean and standard deviation to achieve on target results with minimal variation. It demonstrates benefits via examples where experimenters took advantage of making multiple measurements for every run in their design. Newcomers to statistical design of experiments (DOE) often overlook this opportunity to achieve more robust operating conditions. Attend this webinar to master DOEs aimed at meeting specifications and doing so with utmost reliability.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, September 25, 2024
Time: 10:00am Central US Time
The goal of robustness studies is to demonstrate that our processes will be successful upon implementation in the field when they are exposed to anticipated noise factors. Join this webinar to learn about the assumptions and underlying concepts that need to be understood when setting out to conduct a robustness study, and what kind of design you need to fit each situation.
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.
This presentation provides practical aspects for combining mixture, process and categorical variables into one optimal experiment-design using innovative "KCV" models. Learn about structuring combined designs with a case study on making delightful chocolate chip cookies.
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.
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.
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.
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!).
Mejore su habilidad en diseños de experimentos (DOE) con esta herramienta de prueba multifactorial en esta sesión informativa. Una demostración breve demuestra porque DOE es tan efectivo en acelerar R&D (investigación y desarrollo) y optimización de procesos y productos. En este seminario descubrirá como DOE encuentra los factores vitales más importantes y revela interacciones innovadoras.
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.
Complete conference information here.
Hour 1: Eric Coppens (ONDRAF/NIRAS) - DoE in the field of nuclear waste management Slides | Video
Hour 2: Richard Williams (Richard Scott Williams LLC) - RSM Saves the Circuit Board Slides | Video
Hour 3: Martin Bezener (Stat-Ease) - The Latest & Greatest in Design-Expert® and Stat-Ease® 360 Slides | Video
Complete conference information here.
Hour 1: Mark Anderson (Stat-Ease) - Masterful experiment delivers delectable chocolate chip cookies Slides | Video
Hour 2: Doug Scott (Optimal Formulations LLC) - Application of DOE in the Development of an Alcoholic Beverage Slides | Video
Hour 3: Steve Zagarola (NWCPE) - What Teaching and the Practice of DOE Teaches Us About Deriving Meaning from any Data Slides | Video
This practically focused webinar provides tips and tricks for making the most from every response analysis, particularly for optimization of processes and products (mixture formulations). See how to apply sophisticated selection tools in Stat-Ease software to develop your best-possible predictive model. Gain insights on vital fit statistics such as predicted R-squared. Get a feel for picking from alternative models and when to press ahead to achieve your mission for process and/or product improvement. This talk is a must for all users of Design-Expert® and Stat-Ease® 360.
Discover what you need to know about the diagnostic plots used to validate an analysis of variance (ANOVA). Learn a little about how these plots are made and more importantly, how to interpret the signals that indicate problems with the analysis. In this webinar, we will explore how careful residual analysis can be key to a successful DOE.
By removing known sources of variation such as day-by-day, blocking reduces error in statistical analysis, thus providing higher power for estimating effects and greater precision for fitting response surfaces. The technique, however, encounters limitations in some settings. This quick, 30-minute webinar will discuss the basics of blocking and demonstrate fixed vs. random block effects using the powerful, yet intuitive Stat-Ease® 360 software.
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.
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!
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.
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!
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.
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.
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 details the cost-saving mixture-process models developed by Scott Kowalski, John Cornell and Geoff Vining (KCV). Design-Expert® software 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.
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