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 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
• pharmacokinetics

Examples of curves that need to be described with a more flexible fit include:

• complex growth curves
• chromatograms
• spectroscopy (NIR, NMR, and Raman)
• manufacturing sensor data over time