Math 70 Spring 2020
Elements of Multivariate Statistics and Statistical Learning

Office hours: Monday 10:00–12:00 and Friday 2–3:30.

Instructor: Prof. Eugene Demidenko

Prerequisites: Math 40 or by a special permission of the instructor.

Textbooks:

• Demidenko E (2020). Advanced Statistics with Applications in R. Hoboken, NJ: Wiley: www.eugened.org
• Hastie T, Tibshirani R, Friedman J (2009). The Elements of Statistical Learning, 2d ed, Springer. Provided electronically on canvas.

Additional reading:

• D Zelterman, Applied Multivariate Statistics with R, Springer, 2015.
• WK Härdle, L Simar, Applied Multivariate Statistical Analysis, Springer, 2007. Provided electronically on canvas.

Course Objectives:

1. Provide theoretical basis for multivariate statistical analysis and optimal statistical hypothesis testing, point and interval estimation.
2. Learn about modern statistical methods of statistical analysis including nonlinear mod- els, data mining, and classification techniques.
3. Get experience in statistical solutions of real-life-high-volume problems, including shape and image analysis, using statistical package R.
4. Preparation for a career in data analysis and statistical problem solutions.
5. Programming in R is required.