|Course Title||Multivariate Statistical Analysis for Ecological Data Using R
|Description||TUITION FEES: The Cost To Learner displayed in the Course Details is an approximate amount. Please refer to the 'Class Cost to Learner' displayed in the Class Details for the specific class session you wish to attend. Additional information is available at the NCTC website.
Note: Tuition is not applicable to FWS, BLM and NPS employees
This course covers a variety of descriptive and inferential multivariate statistical methods that are useful for analyzing biological data. Participants use computers to analyze ecological data and apply the various multivariate procedures covered by the instructor. Several case studies of multivariate techniques applied to field data are discussed. Rice Virtual Lab in Statistics
- Identify the basic concepts of matrix algebra, eigenvalues, eigenvectors, and multivariate normality;
- Demonstrate methods for displaying relationships and position (principal components analysis, factor analysis, biplot displays, correspondence analysis, multidimensional scaling, and cluster analysis);
- Perform procedures for group separation (MANOVA, canonical variate analysis, discriminant analysis, logistic regression);
- Describe techniques for determining relationships between sets of variables (canonical correlation analysis and canonical correspondence analysis); and
- Analyze repeated measures.
Anyone responsible for collecting, analyzing, and/or interpreting multi-variable data.
|Delivery Method||Instructor Led
|Course Content Contact||ERIC KELCHLIN; Phone: 304.876.7453; Email: email@example.com;
|Extracted from DOI LEARN on||9/19/2014 11:06:30 AM