Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Lukas Meier: Catalogue data in Autumn Semester 2016

Name Dr. Lukas Meier
Address
Seminar für Statistik (SfS)
ETH Zürich, HG G 15.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 97 49
E-maillukas.meier@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~meier/
DepartmentMathematics
RelationshipLecturer

NumberTitleECTSHoursLecturers
401-0620-00LStatistical Consulting0 credits0.1KM. Kalisch, L. Meier
AbstractThe Statistical Consulting service is open for all members of ETH, including students, and partly also to other persons.
ObjectiveAdvice for analyzing data by statistical methods.
ContentStudents and researchers can get advice for analyzing scientific data, often for a thesis.
We highly recommend to contact the consulting service when planning a project, not only towards the end of analyzing the resulting data!
Prerequisites / NoticeThis is not a course, but a consulting service. There are no exams nor credits.

Contact: beratung@stat.math.ethz.ch . Tel. 044 632 2223. See also http://stat.ethz.ch/consulting

Requirements: Knowledge of the basic concepts of statistics is desirable.
401-0625-01LApplied Analysis of Variance and Experimental Design Information 5 credits2V + 1UL. Meier
AbstractPrinciples of experimental design. One-way analysis of variance. Multi-factor experiments and analysis of variance. Block designs. Latin square designs. Split-plot and strip-plot designs. Random effects and mixed effects models. Full factorials and fractional designs.
ObjectiveParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
ContentPrinciples of experimental design. One-way analysis of variance. Multi-factor experiments and analysis of variance. Block designs. Latin square designs. Split-plot and strip-plot designs. Random effects and mixed effects models. Full factorials and fractional designs.
LiteratureG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Prerequisites / NoticeThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
http://stat.ethz.ch/events/zukost
Course language is English or German and may depend on the speaker.
701-0105-00LApplied Statistics for Environmental Sciences3 credits2GC. Bigler, U. Brändle, M. Kalisch, L. Meier
AbstractStatistical methods from current publications in environmental sciences are presented and applied. Students are enabled to understand the methods, clean datasets, analyse them using the software package R and present the results in a suitable form. They will be able to describe strengths and weaknesses of the methods for given fields of application.
ObjectiveStudents are able to
- use suitable statistical methods for data analysis in their subject area.
- characterize data sets using explorative methods
- check the suitability of data sets to answer a given question, prepare data sets for import to a statistics program and conduct the analysis.
- interpret statistical analyses and process them graphically for use in presentations and publications.
- describe the basics of statistical methods used in current publications.
- use the software package R for statistical analysis
ContentStatistische Methoden: Regression (lineare Modelle; generalisierte lineare Modelle; GLMs); Varianzanalyse; gemischte Modelle für gruppierte Daten (mixed-effects models); Fragebogenstatistik; Tests (t Test; Chiquadrat Test; Fisher Test); Power-Analyse

Werkzeuge: Explorative Datenanalyse für Hypothesenbildung; Auswahlverfahren für geeignete statistische Verfahren; Datenaufbereitung (Excel -> R; Datenbereinigung); graphische Darstellung von Resultaten; statistische Verfahren in Publikationen erkennen
Wir arbeiten mit dem Softwarepaket R.

Form: Im Wochenrhythmus finden alternierend Einführungen in eine neue Methode und Übungsstunden zum Thema statt.
Prerequisites / NoticeBesuch von "Mathematik IV: Statistik" oder vergleichbare Lehrveranstaltung