# Search result: Catalogue data in Spring Semester 2019

Number Title Type ECTS Hours Lecturers Statistics Master The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible. Core CoursesIn each subject area, the core courses offered are normally mathematical as well as application-oriented in content. For each subject area, only one of these is recognised for the Master degree. Multivariate Statistics 401-6102-00L Multivariate Statistics W 4 credits 2G N. Meinshausen Abstract Multivariate Statistics deals with joint distributions of several random variables. This course introduces the basic concepts and provides an overview over classical and modern methods of multivariate statistics. We will consider the theory behind the methods as well as their applications. Objective After the course, you should be able to:- describe the various methods and the concepts and theory behind them- identify adequate methods for a given statistical problem- use the statistical software "R" to efficiently apply these methods- interpret the output of these methods Content Visualization / Principal component analysis / Multidimensional scaling / The multivariate Normal distribution / Factor analysis / Supervised learning / Cluster analysis Lecture notes None Literature The course will be based on class notes and books that are available electronically via the ETH library. Prerequisites / Notice Target audience: This course is the more theoretical version of "Applied Multivariate Statistics" (401-0102-00L) and is targeted at students with a math background. Prerequisite: A basic course in probability and statistics. Note: The courses 401-0102-00L and 401-6102-00L are mutually exclusive. You may register for at most one of these two course units. 401-0102-00L Applied Multivariate Statistics W 5 credits 2V + 1U F. Sigrist Abstract Multivariate statistics analyzes data on several random variables simultaneously. This course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics including visualization, dimension reduction, supervised and unsupervised learning for multivariate data. An emphasis is on applications and solving problems with the statistical software R. Objective After the course, you are able to:- describe the various methods and the concepts behind them- identify adequate methods for a given statistical problem- use the statistical software R to efficiently apply these methods- interpret the output of these methods Content Visualization, multivariate outliers, the multivariate normal distribution, dimension reduction, principal component analysis, multidimensional scaling, factor analysis, cluster analysis, classification, multivariate tests and multiple testing Lecture notes None Literature 1) "An Introduction to Applied Multivariate Analysis with R" (2011) by Everitt and Hothorn 2) "An Introduction to Statistical Learning: With Applications in R" (2013) by Gareth, Witten, Hastie and TibshiraniElectronic versions (pdf) of both books can be downloaded for free from the ETH library. Prerequisites / Notice This course is targeted at students with a non-math background. Requirements:==========1) Introductory course in statistics (min: t-test, regression; ideal: conditional probability, multiple regression)2) Good understanding of R (if you don't know R, it is recommended that you study chapters 1,2,3,4, and 5 of "Introductory Statistics with R" from Peter Dalgaard, which is freely available online from the ETH library)An alternative course with more emphasis on theory is 401-6102-00L "Multivariate Statistics" (only every second year).401-0102-00L and 401-6102-00L are mutually exclusive. You can register for only one of these two courses.
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