# 401-0102-00L  Applied Multivariate Statistics

 Semester Spring Semester 2019 Lecturers F. Sigrist Periodicity yearly recurring course Language of instruction English

### Courses

NumberTitleHoursLecturers
401-0102-00 VApplied Multivariate Statistics2 hrs
 Mon 15:15-17:00 HG F 3 »
F. Sigrist
401-0102-00 UApplied Multivariate Statistics
The exercise class originally scheduled on Monday, 15 April will take place on Friday, 12 April, 11-13 in HG D 7.1.
1 hrs
 Mon/2w 08:15-10:00 HG D 1.1 » 12.04. 11:15-13:00 HG D 7.1 »
F. Sigrist

### Catalogue data

 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.

### Performance assessment

 Performance assessment information (valid until the course unit is held again) Performance assessment as a semester course ECTS credits 5 credits Examiners F. Sigrist Type session examination Language of examination English Repetition The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. Mode of examination written 120 minutes Written aids Closed book; simple pocket calculator with no communication capability This information can be updated until the beginning of the semester; information on the examination timetable is binding.

### Learning materials

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### Groups

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### Restrictions

 There are no additional restrictions for the registration.

### Offered in

ProgrammeSectionType
Biology MasterElective Compulsory Master Courses I: ComputationW
DAS in Data ScienceStatisticsW
Earth Sciences BachelorMajor ElectivesW
Statistics MasterMultivariate StatisticsW
Environmental Sciences BachelorMethodes of Statistical Data AnalysisW
Environmental Sciences BachelorMethodes of Statistical Data AnalysisW