Many scientists and engineers have data that are represented by a curve, and using curves in analysis is challenging. Curves are often summarized into a few parameters for analysis. However, parameterizing a curve can be difficult, time-consuming, and most of the data is left behind. In today’s workplace, doing more with less is critical; luckily, JMP Statistical Discovery Software can help.
In this webinar, we’ll learn more about analyzing two types of curves: curves that can be described by a formula and curves that need to be described with a flexible fit. Examples of curves that can be described with a formula include:
- dose response curves
- enzyme kinetics
- growth and decay curves
Examples of curves that need to be described with a more flexible fit include:
- complex growth curves
- spectroscopy (NIR, NMR, and Raman)
- manufacturing sensor data over time
In this webinar, you’ll learn how tools in JMP will help you better understand the variation in the shape of curves and how to use curves in Design of Experiments and modeling to better understand how input variables effect the shape of curves.
- Andrea Coombs, Senior Systems Engineer JMP
- Kemal Oflus, Principal Systems Engineer JMP
- Russ Wolfinger, Director of Scientific Discovery and Genomics, Distinguished Research Fellow JMP