The Multivariate Flavors of JMP®: From Continuous to Categorical to Discrete to Functional (EU 2018 425)
Feb 16, 2018 9:20 AM
| Last Modified: Mar 2, 2018 1:07 PM
Level: Intermediate Laura Castro-Schilo, JMP Research Statistician Tester, SAS Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
Multivariate data analysis has become an essential skill as the amount of data has skyrocketed and companies become more data-driven in their decision making. In this extended breakout session, we showcase JMP and its unique visual and interactive approach to multivariate data analysis using a variety of approaches that rely on continuous, categorical, discrete and functional data.
We will begin with a foundational discussion of the essence of multivariate analysis: the idea that information contained in large numbers of variables can often be efficiently represented with a smaller number of variables. We then give an overview of general tools for analyzing multivariate data in JMP.
The session will be given in two parts. The introduction will use data from sensory analysis and consumer research to motivate and demonstrate classic unsupervised multivariate methods such as principal components analysis, multiple correspondence analysis and a new technique in JMP 14, multiple factor analysis. The second part will cover supervised methods for multivariate data using partial least squares and functional principal components. To illustrate these supervised methods we will build models to predict the yield of a batch manufacturing process with data from multiple signal streams.
Throughout, we will give clear guidelines as to when each analytical technique is appropriate and will highlight the most important and useful software options.