Markus Kalisch: Catalogue data in Autumn Semester 2023 |  |
| 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. See also http://stat.ethz.ch/consulting Requirements: Knowledge of the basic concepts of statistics is desirable. | ||||||||||||||||||||||||||||||||
| 401-3622-DRL | Statistical Modelling   Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 | 2 credits | 4G | M. Kalisch | |||||||||||||||||||||||||||||
| Abstract | In regression, the dependency of a random response variable on other variables is examined. We consider the theory of linear regression with one or more covariates, high-dimensional linear models, nonlinear models and generalized linear models, model choice and nonparametric models. Several numerical examples will illustrate the theory. | ||||||||||||||||||||||||||||||||
| Learning objective | - Thorough, theoretical understanding of linear regression - Overview of several extensions of linear regression - Ability to correctly apply the methods learned in simple data examples | ||||||||||||||||||||||||||||||||
| Content | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle, Modellsuche, Residuenanalyse, nicht-parametrische Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This is the course unit with former course title "Regression". Credits cannot be recognised for both courses 401-3622-00L Statistical Modelling and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | ||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||
| 401-3622-00L | Statistical Modelling | 7 credits | 4G | M. Kalisch | |||||||||||||||||||||||||||||
| Abstract | In regression, the dependency of a random response variable on other variables is examined. We consider the theory of linear regression with one or more covariates, high-dimensional linear models, nonlinear models and generalized linear models, robust methods, model choice and nonparametric models. Several numerical examples will illustrate the theory. | ||||||||||||||||||||||||||||||||
| Learning objective | Introduction into theory and practice of a broad and popular area of statistics, from a modern viewpoint. | ||||||||||||||||||||||||||||||||
| Content | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | ||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This is the course unit with former course title "Regression". Credits cannot be recognised for both courses 401-3622-00L Statistical Modelling and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | ||||||||||||||||||||||||||||||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl | |||||||||||||||||||||||||||||
| Abstract | About 3 talks on applied statistics. | ||||||||||||||||||||||||||||||||
| Learning objective | See how statistical methods are applied in practice. | ||||||||||||||||||||||||||||||||
| Content | There will be about 3 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. | ||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||||||||
| 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". | ||||||||||||||||||||||||||||||||
| 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/ | ||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||