Developer Tutorial: Modeling Spectral Data Using JMP Pro 17
Statistics, Predictive Modeling and Data Mining
This session is for JMP or JMP Pro users who understand basic predictive modeling principles, have used JMP Pro for predictive modeling, and need to model curved or spectral data.
Curves and spectra are fundamental to understanding many scientific and engineering applications. As a result, curves or spectral data are created by many types of test and manufacturing equipment. When these data are used as part of a designed experiment or a machine learning application, most software requires the practitioner to extract features from the data prior to modeling. This leads to models that are more difficult to interpret and are less accurate than models that treat spectral/curve data as first-class citizens.
JMP Pro makes it easy to directly model using curved or spectral data in designed experiments and machine learning applications. In this tutorial Chris Gotwalt, JMP Chief Data Scientist, and Ryan Parker, Senior Research Statistical Developer, give an overview of functional data analysis in JMP Pro. They will focus on new capabilities in JMP Pro 17, such was wavelet analysis, that make it easier than ever to analyze spectral data from NMR, mass spectroscopy, chromatography, and many other types of analysis common in the chemical, pharmaceutical, and biotech industries. They explain how and why JMP Pro handles these data. The session includes time for Q&A.
This JMP Developer Tutorial covers: functional design of experiments, functional machine learning, wavelet models and spectral data preprocessing.