Applied Multivariate Statistics
Spring semester 2017
General information
Lecturer  Fabio Sigrist 

Assistants  Samarth Shukla, Shu Li, Patricia Calvo Perez 
Lectures  Mon 810 HG E 5 >> 
Exercises  Mon 1517 (every second week) HG E 1.2 >> 
Course catalogue data  >> 
Course content
Multivariate statistics studies methods to analyze data on several random variables simultaneously. This course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics, with an emphasis on applications and solving problems with the statistical software "R".
Announcements

Februar 3, 2017:
Beginning of lecture: Monday, 20/02/2017 (Exercises start on 27/02/2017 at 15:15).
Examples in the lecture and hints as well as solutions to the exercises will be based on the statistical software R. This is a freely available open source program that works on all platforms and has become worldwide standard for data analysis. It can be downloaded from CRAN. A R Tutorial can be found here.
There will be a 120minute written exam during the regular ETH exam sessions. The exam is a "closed book" exam, a simple pocket calculator with no communication capability is permitted. The exam covers all topics which were discussed and/or applied during either the lectures or the exercises. Upon passing the exam, the course will be awarded 5 ECTS credit points.
PhD students who would like to obtain credit points but do not need to take the exam and obtain a grade need to sign up with the lecturer at the beginning of the semester and hand in 5 wellsolved exercises.
Course materials
The schedule is subject to minor modifications. The slides will be uploaded before each lecture.
Week  Topic 

Week 1  Introduction & visualization 
Week 2  Visualization & outliers 
Week 3  Principal component analysis 1 
Week 4  Principal component analysis 2 
Week 5  Multidimensional scaling 
Week 6  Factor analysis 
Week 7  Cluster analysis 
Week 8  Classification 1: discriminant analysis & logistic regression 
Week 9  Extending univariate methods 
Week 10  Classification 2: trees & random forest 
Week 11  Models for repeated measures data 
Week 12  Selection of additional machine learning methods 
Literature:
Everitt and Hothorn (2011), An Introduction to Applied Multivariate Analysis with R, Springer
James, Witten, Hastie, and Tibshirani (2013), An Introduction to Statistical Learning, Springer
P. Dalgaard (2008), Introductory Statistics with R, Springer
Exercise classes
Exercise classes are held approx. every second week starting from 27/02/2017 in HG E 1.2. Please install R and RStudio and bring your laptop to the exercise classes, if possible.
Series and solutions
The series' solutions should be send to the assistants by email and by the due date. Please mark which part of your solutions are unclear and should be corrected.
There is no testat requirement.
Exercises  Solutions  Exercise/due date 

Series 1  Solutions 1  27.2.17/6.3.17 
Series 2  Solutions 2  13.3.17/20.3.17 
Series 3  Solutions 3  27.3.17/3.4.17 
Series 4  Solutions 4  10.4.17/19.4.17 
Series 5  Solutions 5  15.5.17/22.5.17 
Series 6  Solutions 6  29.5.17/5.6.17 