[Presentation summary]
With a unique combination of an advanced statistical platform and design of experiments, JMP helps improve competitiveness and engineering efficiency across organizations through analytics and statistical methods.
In this presentation, we will introduce how the experimental design tools provided in JMP 17 can assist users at various stages. In the first half of the presentation, we will introduce DOE with navigation (English name: Easy DOE). The Navigated DOE is a tool that revolutionizes the way novice experimenters approach their work.
This tool makes the design-of-experiment workflow, from planning to analysis, more accessible to people of all skill levels. Learn how this innovative solution simplifies the process from experimental design to analysis, allowing beginners to start using experimental design with confidence.
Taking design of experiments further, designs may incorporate features such as blocking factors or combinations of factors that are difficult to change and those that are easy to change (split experiments). These designs are properly analyzed using a mixture model.
Mixed models (responses are normally distributed) and generalized linear mixed models (responses are discrete count, binary, binomial (y/n) distributed) enable fast and accurate analysis for effective decision making I'll make it. Discovering and utilizing these techniques may seem complicated at first, but it doesn't have to be difficult.
In fact, as developers of JMP, we designed it with simplicity in mind, making it accessible and easy to use for researchers and analysts of all levels. See how these features expand the range of plans and responses you can analyze in JMP Pro's user-friendly environment.
[Presenter profile]
Ryan Lekivetz: Manager, Advanced Analytics R&D, JMP Development, JMP Statistical Discovery LLC.
Manager of the team developing JMP's design of experiments (DOE) and reliability platform. in Statistics from Simon Fraser University (Burnaby, British Columbia, Canada) and has published papers on DOE topics in peer-reviewed academic journals. In addition, by applying DOE to daily life, we are exploring ways to make use of this research at home.
Elizabeth A. Claassen :Sr Research Statistician Developer, JMP Development, JMP Statistical Discovery LLC.
Senior Research Statistics Developer with over 10 years of experience in JMP and SAS. His main area of responsibility is generalized linear mixed models (GLMM), and he is the development leader for GLMM in JMP Pro. He received his master's degree and doctorate in statistics from the University of Nebraska-Lincoln, where he received the Holling Family Award for Outstanding Teaching in the College of Agricultural Sciences and Natural Resources.
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