Name | Dr. Markus Kalisch |
Address | Seminar für Statistik (SfS) ETH Zürich, HG G 15.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
Telephone | +41 44 632 34 35 |
markus.kalisch@stat.math.ethz.ch | |
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. | ||||
401-0643-13L | Statistics II | 3 credits | 2V + 1U | M. Kalisch | |
Abstract | Vertiefung von Statistikmethoden. Nach dem detailierten Fundament aus Statistik I liegt nun der Fokus auf konzeptueller Breite und konkreter Problemlösungsfähigkeit mit der Statistiksoftware R. | ||||
Objective | Nach diesem Kurs können Sie mit der Statistiksoftware R Daten einlesen, auf vielfältige Art verarbeiten und Grafiken für Berichte oder Vorträge exportieren. Sie verstehen die Konzepte von Methoden wie Lineare Regression (mit Faktoren, Interaktion, Modellwahl), ANOVA (1-weg, 2-weg), Chi-Quadrat-Test, Fisher-Test, GLMs, Mixed Models, Clustering, PCA und können diese mit der Statistiksoftware R in der Praxis umsetzen. Zudem kennen Sie die Grundprinzipien von gutem experimentellem Design und können bestehende Studien kritisch hinterfragen. | ||||
401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
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. | ||||
406-0603-AAL | Stochastics (Probability and Statistics) Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | 4 credits | 9R | M. Kalisch | |
Abstract | Introduction to basic methods and fundamental concepts of statistics and probability theory for non-mathematicians. The concepts are presented on the basis of some descriptive examples. Learning the statistical program R for applying the acquired concepts will be a central theme. | ||||
Objective | The objective of this course is to build a solid fundament in probability and statistics. The student should understand some fundamental concepts and be able to apply these concepts to applications in the real world. Furthermore, the student should have a basic knowledge of the statistical programming language "R". | ||||
Content | From "Statistics for research" (online) Ch 1: The Role of Statistics Ch 2: Populations, Samples, and Probability Distributions Ch 3: Binomial Distributions Ch 6: Sampling Distribution of Averages Ch 7: Normal Distributions Ch 8: Student's t Distribution Ch 9: Distributions of Two Variables From "Introductory Statistics with R (online)" Ch 1: Basics Ch 2: The R Environment Ch 3: Probability and distributions Ch 4: Descriptive statistics and tables Ch 5: One- and two-sample tests Ch 6: Regression and correlation | ||||
Literature | - "Statistics for research" by S. Dowdy et. al. (3rd edition); Print ISBN: 9780471267355; Online ISBN: 9780471477433; DOI: 10.1002/0471477435 From within the ETH, this book is freely available online under: http://onlinelibrary.wiley.com/book/10.1002/0471477435 - "Introductory Statistics with R" by Peter Dalgaard; ISBN 978-0-387-79053-4; DOI: 10.1007/978-0-387-79054-1 From within the ETH, this book is freely available online under: http://www.springerlink.com/content/m17578/ | ||||
701-0105-00L | Applied Statistics for Environmental Sciences | 3 credits | 2G | C. Bigler, 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 (ANOVA); gemischte Modelle für gruppierte Daten (mixed-effects models); Fragebogenstatistik; Tests (t Test) 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 |