Hi all,
Thank you for attending the first JMP Meet-up in Norway. Special thanks to Patrick Galler (@Ressel) for hosting us at the Aker BioMarine office. As a follow-up, I wanted to share some helpful resources related to Design of Experiments (DoE) since we talked a lot about that, along with the presentation from our session (attached with all links provided inside) about how to nurture data driven mindset from the individual to the whole organization.
For those of you just starting with DoE, the Easy DoE platform in JMP provides an end-to-end guided approach, allowing you to define, enter data, and analyze experiments seamlessly. You can explore it here:
If you're looking for more flexibility, the Custom Design in JMP can handle various experimental situations, including screening, optimization, and mixture problems, even with hard-to-change factors. The most important thing is that you don't need to know which design to use prior to designing your experiments. You just have to add your factors(input) your responses(output) and interaction or power terms (depending on what you are searching to find). This brief video provides a great introduction:
For a more in-depth look, this three-part workshop series offers great examples of how to use DoE effectively:
- Getting Started with Design of Experiments – Access Here
Since Definitive Screening Designs (DSD) came up in our discussion, here’s a great article from Bradley Jones on when (and when not) to use them:
Please feel free to reach out with any questions.
Spyros
