Markus Kalisch: Catalogue data in Spring Semester 2017 |  |
| 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. | ||||
| Learning 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 or 044 632 34 30 Requirements: Knowledge of the basic concepts of statistics is desirable. | ||||
| 401-0643-00L | Statistics I  | 3 credits | 2V + 1U | 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 objective | Grundverständnis für die Gesetze des Zufalls und des Denkens in Wahrscheinlichkeiten. Kenntnis von Methoden zur Darstellung von Daten und zu ihrer quantitativen Interpretation unter Berücksichtigung der statistischen Unsicherheit. | ||||
| Content | Modelle und Statistik für Zähldaten: Diskrete Wahrscheinlichkeitsmodelle, Binomial-Verteilung, Tests und Vertrauensintervalle für eine Wahrscheinlichkeit, Poisson-Verteilung und deren Statistik, weitere Verteilungen. Modelle und Statistik für Messdaten: Beschreibende Statistik, Zufallsvariablen mit Dichten, t-Test und Wilcoxon-Test und zugehörige Vertrauensintervalle. Regression: Das Modell der linearen Regression, Tests und Vertrauensintervalle, Residuenanalyse. | ||||
| Lecture notes | Es steht ein kurzes Skript zur Verfügung. | ||||
| Literature | - W. A. Stahel, Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, 4. Aufl., Vieweg, Braunschweig/Wiesbaden, 2002 | ||||
| Prerequisites / Notice | Voraussetzungen: Grundlegende Mathematik-Kenntnisse wie sie im ersten Semester erworben werden. Diese Lerneinheit:wurde bis zum HS 2013 als 401-0643-00L Statistik angeboten. | ||||
| 401-4620-00L | Statistics Lab  Number of participants limited to 27. | 6 credits | 2S | M. Kalisch, M. H. Maathuis, L. Meier, N. Meinshausen | |
| Abstract | "Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting. Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar. | ||||
| Learning objective | - gain initial experience in the consultancy process - carry out a consultancy session and produce a report - apply theoretical knowledge to an applied problem After the course, students will have practical knowledge about statistical consulting. They will have determined the scientific problem and its context, enquired the design of the experiment or data collection, and selected the appropriate methods to tackle the problem. They will have deepened their statistical knowledge, and applied their theoretical knowledge to the problem. They will have gathered experience in explaining the relevant mathematical and software issues to a client. They will have performed a statistical analysis using R (or SPSS). They improve their skills in writing a report and presenting statistical issues in a talk. | ||||
| Content | Students participate in consulting meetings at the SfS. Several consulting dates are available for student participation. These are arranged individually. -During the first meeting the student mainly observes and participates in the discussion. During the second meeting (with a different client), the student leads the meeting. The member of the consulting team is overseeing (and contributing to) the meeting. -After the meeting, the student performs the recommended analysis, produces a report and presents the results to the client. -Finally, the student presents the case in the weekly course seminar in a talk. All students are required to attend the seminar regularly. | ||||
| Lecture notes | n/a | ||||
| Literature | The required literature will depend on the specific statistical problem under investigation. Some introductory material can be found below. | ||||
| Prerequisites / Notice | Prerequisites: Sound knowledge in basic statistical methods, especially regression and, if possible, analysis of variance. Basic experience in Data Analysis with R and/or SPSS. Useful background lectures and material: -Applied Statistical Regression (Dr. Marcel Dettling) http://stat.ethz.ch/education/semesters/as2010/semesters/as2010/asr -Angewandte statistische Regression, mit Ergänzung (Prof. Werner Stahel, Dr. Markus Kalisch) Script: http://stat.ethz.ch/~stahel/courses/regression/ -Applied Analysis of Variance and Experimental Design (Prof. M Müller) http://stat.ethz.ch/education/semesters/as2010/anova -W. Stahel, Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, (5. Auflage), Vieweg, 2005. Useful material on Statistical Software (R and/or SPSS): -401-6215-00L Using R for Statistical Data Analysis and Graphics (Dr. M. Mächler, Dr. A. J. Papritz, Dr. C. B. Schwierz). An older version of this course can be found on: http://stat.ethz.ch/ stahel/courses/R/ -An Introduction to R. http://stat.ethz.ch/CRAN/doc/manuals/R-intro.pdf -SPSS Course and Exercises: ftp://stat.ethz.ch/U/sfs/SPSSKurs/ -Andy Field, Discovering Statistics Using SPSS, 3rd Edition, 2009, SAGE. | ||||
| 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, S. van de Geer | |
| Abstract | 5 to 6 talks on applied statistics. | ||||
| Learning objective | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin. | ||||
| Content | In 5-6 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. 3 bis 4 der Vorträge stehen in der Regel unter einem Semesterthema. | ||||
| Lecture notes | Bei manchen Vorträgen werden Unterlagen verteilt. Eine Zusammenfassung ist kurz vor den Vorträgen im Internet unter http://stat.ethz.ch/talks/zukost abrufbar. Ankündigunen der Vorträge werden auf Wunsch zugesandt. | ||||
| Prerequisites / Notice | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm. Koordinator M. Kalisch, Tel. 044 632 3435 Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. 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. The course will be based on the book "Statistics for research" by S. Dowdy et.al. and on the book "Introductory Statistics with R" by P. Dalgaard. | ||||
| Learning 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". The main topics of the course are: - Introduction to probability - Common distributions - Binomialtest - z-Test, t-Test - Regression | ||||
| Content | From "Statistics for research": 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 [Regression] From "Introductory Statistics with R": Ch 1: Basics Ch 2: Probability and distributions Ch 3: Descriptive statistics and tables Ch 4: One- and two-sample tests Ch 5: 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/ | ||||