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"JMP® Pro: Analyzing Curves and Profiles Using the Functional Data Explorer" Course Homepage

Summary: This course helps you recognize and model functional data using JMP Pro. It teaches you to use the data as a response, such as the outcome for a designed experiment, or as new covariates or features, such as in a multivariate analysis.

Functional data are defined as a function, profile, or curve. They are a series of observations over time or any other continuous variable. These data can be modeled so that changes in the shape can be associated with changes in other variables.

Modalities:

live online with instructor -- This course is available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.

Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.

Learning Objectives:

Recognize functional data and their uses.
Preprocess data to suit the functional data analysis.
Select a model of the functions.
Obtain optimal representations of the functions.
Use the new representations as the responses in the analysis of a designed experiment.

Course Outline:

Explore Functional Data

Define functional data and their forms.

Preprocess Functional Data

Identify different formats of functional data.
Clean up functional data (recoding tasks).
Transform functional data output (centering and scaling, other common transformations).
Align functional data input.

Fit Models of Functional Data

Describe a basis function expansion as a model for a continuous function.
Optimize trade-off between model bias and variance with smoothing.
Select splines or Fourier basis for the model.
Fit the model.
Select the best model.
Diagnose the model.

Extract Functional Principal Components

Describe the principal components analysis of continuous functions.
Derive and use functional principal component scores.

Functional Analysis of Experiments

Identify the goals of the analysis of an experiment.
Estimate factor effects on the functional principal components.

Capstone Case Studies

Select one of several new cases chosen from different industries to practice a complete functional data analysis.

Advanced Statistical Modeling

Design of Experiments

Predictive Modeling and Machine Learning

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