***This is a course guideline. Students should contact instructor for the updated information on current course syllabus, textbooks, and course content***
Prerequisites: MATH 3349
Course Description: Multivariate analysis is a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables. The techniques provide a method for information extraction, regression, or classification. This includes applications of data sets using statistical software.
Text: Applied Multivariate Statistical Analysis (6th Edition), Pearson. Richard A. Johnson, Dean W. Wichern. ISBN: 978-0131877153 (Required)
Using R With Multivariate Statistics (1st Edition). Schumacker, R. E. SAGE Publications. ISBN: 978-1483377964 (Optional)
- Understand how to use R and R Markdown
- Understand matrix algebra using R
- Understand the geometry of a sample and random sampling
- Understand the properties of multivariate normal distribution
- Make inferences about a mean vector
- Compare several multivariate means
- Identify and interpret multivariate linear regression models
|Introduction to R Markdown, Review of R commands||Notes|
|Introduction to Multivariate Analysis||Ch.1|
|Matrix Algebra, R Matrix Commands||Ch.2|
|Sample Geometry and Random Sampling||Ch.3|
|Multivariate Normal Distribution||Ch.4|
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