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 |
lukas.meier@stat.math.ethz.ch | |
URL | http://stat.ethz.ch/~meier/ |
Department | Mathematics |
Relationship | Lecturer |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
401-0620-00L | Statistical Consulting | 0 credits | 0.1K | M. Kalisch, L. Meier | |
Abstract | The Statistical Consulting service is open for all members of ETH, including students, and partly also to other persons. | ||||
Objective | Advice for analyzing data by statistical methods. | ||||
Content | Students 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 / Notice | This 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. | ||||
401-0625-01L | Applied Analysis of Variance and Experimental Design | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | ||||
Objective | Participants 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. | ||||
Content | Principles 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. | ||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | ||||
Prerequisites / Notice | The 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. | ||||
401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl | |
Abstract | About 5 talks on applied statistics. | ||||
Objective | See how statistical methods are applied in practice. | ||||
Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||
Prerequisites / Notice | This 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. | ||||
701-0105-00L | Applied Statistics for Environmental Sciences | 3 credits | 2G | C. Bigler, U. Brändle, M. Kalisch, L. Meier | |
Abstract | Statistical 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. | ||||
Objective | Students 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 | ||||
Content | Statistische 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 / Notice | Besuch von "Mathematik IV: Statistik" oder vergleichbare Lehrveranstaltung |