These courses teach intermediate-to-advanced users of JMP® software specialized methods such as categorical analysis, predictive modeling, principles components analysis (PCA), partial least squares (PLS), text (mining) exploration, and function data (analysis) exploration. Several courses incorporate the use of additional analytical power offered by JMP Pro.
Analyzing Discrete Responses Course This course teaches you how to analyze discrete (or categorical) data or outcomes using association, contingency tables, stratification, correspondence analysis, logistic regression, generalized linear models,...
Analyzing and Modeling Multidimensional Data Course This course is for JMP users who work with data that have many variables. The course demonstrates various ways to examine high-dimensional data in fewer dimensions, as well as patterns that exist in...
[course_custom_schema]Analysis of Dose-Response Curves Course This course helps you fit a sigmoid model to your dose-response curve data, select the best model, assess the model fit, determine whether two curves are parallel, determine the EC50 or...
Finding Important Predictors Course This course teaches you techniques for fitting statistical models to identify important variables. Manual, graphical, and automated variable selection techniques are presented, along with advanced modeling methods....
[course_custom_schema]Predictive Modeling Course This course covers the skills required to develop, assess, tune, compare, and score predictive models using JMP Pro software.This course teaches you how to build and understand predictive models using...
[course_custom_schema]Exploring and Analyzing Patterns in Text Data Course This course teaches you how to find and analyze patterns in textual data using Text Explorer in JMP Pro. A workflow is defined for effective and efficient exploration....
Analyzing Curves and Profiles Using the Functional Data Explorer Course This course helps you recognize and model functional data. It teaches you to use the data as a response, such as the outcome for a designed experiment, or as new covariates or...
