DATA INSIGHT
LIVE WEBINAR
Turning Data into Decisions: Picking the Right Predictive Model
Date: Tuesday, April 14
Time: 2:00–2:30 p.m. ET | 11:00 a.m. PT
Duration: 30 minutes
Registration: FREE
Choosing the right predictive model can feel overwhelming. Many people look for the “best” model, but aren’t sure how to identify which one truly supports their business goals – whether that’s understanding why something happens, predicting what will happen next, or spotting key drivers.
In this 30-minute webinar, we take the guesswork out of this process. Using a practical case study, explore how different models are fitted, compared, and refined, presenting a clear roadmap for future projects.
Learn how to:
- Match the right model to the question at hand.
- Use a structured approach to compare and select models.
- Explore common modeling techniques like regression, decision trees, neural networks, and more.
Whether you are new to predictive modeling or refining an existing approach, this session helps clarify which model to use and when – translating insights into confident decisions.
About the presenters
Oliva Lippincott,
Senior Systems Engineer
Olivia Lippincott is a Senior Systems Engineer for JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software designed for scientists and engineers. She supports sales and customer development for the Mid-Atlantic region of the U.S.
She holds a BS in statistics from the University of North Carolina at Wilmington and an MS in analytics from North Carolina State University.
Clovis Weisbart,
Senior Systems Engineer
Clovis Weisbart is a Senior Systems Engineer at JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software designed for scientists and engineers.
Prior to joining JMP, he worked in the R&D Wet Process Development group at Micron Technology and as an R&D Semiconductor Process Engineer at Keysight Technologies, the leading provider of electronic design and measurement solutions. In both roles, Weisbart used JMP for process control and quality, design of experiments, and predictive modelling. He holds a bachelor’s in engineering physics from the University of California, Berkeley, and a Ph.D. in material science and engineering from the University of Arizona.