Multivariate Data Analysis involves observation and analysis of more than one statistical outcome variable at a time. This technique is used to perform studies across multiple dimensions considering the effects of all variables on the responses. We will use data to learn about:
•Multivariate Analysis of Variance (MANOVA) for comparing population means of several groups;
•Linear Discriminate Analysis (used for pattern recognition, to find linear combinations of features);
•Principal Component Analysis (PCA) convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables (principal components);
•Canonical Correlations which make sense of cross-covariance matrices-2 sets of variables and correlations among the variables leading to linear combinations of dependent and independent variables.
Participants will have the opportunity to work with their data or use a “dummy” database to experience how to do a regression analysis.
•Use multivariate data analysis (MANOVA, Linear Discriminate Analysis, Principal Component Analysis, & Canonical Correlation capabilities of the SPSS program
•Analyze their own or “dummy” data for practice
•Discuss any questions they may have about the program
Date: Tuesday, July 9th, 2013 Time: 9:00am-12:00pm Location: KLIB 219 Group ID: Faculty: Scholarship