Fitting Curves to Non-Linear Data
Statistics, Predictive Modeling and Data Mining
See how to create non-linear models for situations where linear models just won’t work and there are exponents in the model parameters. Science and engineering is full of interesting relationships where the rate of change over time, concentration, or any number of varying inputs defies characterization with linear models. These situations, and half-life, kinetics for growth or decay, pharmacometrics, and sigmoidal potency responses that often require special handling can be addressed using non-linear models. See a 30-minute demo and stay on if you want to join 15-30 minutes of Topic Discussion and Q&A.
This session covers: tips and tricks for effectively working with your non-linear data; setting up and comparing models using JMP’s Fit Curve platform (including new capabilities added in JMP 17); and understanding statistical details related to non-linear models and reports.