A Case Study in Using JMP Statistical Tools to Tackle Chemical Engineering Challenges – Session 1 | AIChE
Sponsor: JMP | This webinar is sponsored by JMP and reflects their views, opinions, and insights. Attendance is free.

A Case Study in Using JMP Statistical Tools to Tackle Chemical Engineering Challenges – Session 1

Session 1: Yield Data Exposed

Discover an easier, faster way to take on your most complex chemical engineering data challenges. Join this two-session webinar and learn how JMP software can help you analyze data more effectively, improve your approach to designing experiments and broaden your knowledge of how to use data to solve problems. Learn how to save your company time and money using the powerful statistical analysis tools you’ll see in action here.  

Through a case study, you’ll walk through the entire experimental process, starting with data gathering and cleanup. You’ll discover which factors are important and design an experiment to efficiently create a model for these factors. You’ll also examine how to analyze the experiment, use the results to set meaningful and robust specification limits and verify that the now-improved process does not drift over time.

See the concepts you learn demonstrated using JMP software:

Session 1: Yield Data Exposed

In this session, you’ll address how cleaning data is not always easy, but a necessity. You’ll follow along as all factors are organized and set within rational spec limits. You’ll see how this helps you choose the best graph design (via the Graph Builder) to visualize and prioritize factors. You’ll then implement Design of Experiments (DOE) to reach the ultimate outcome.

In the next session, Session 2: Insights Revealed via DOE, you'll learn how to analyze this data. Click here to learn more about this webinar. 

You’ll learn:

  • How factors relate to output via mathematical models

  • How to analyze data from multiple sources

  • How to design efficient experiments tailored to specific problems

  • How to evaluate the results of experiments and choose specific settings to maximize value


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