Markus Kalisch: Catalogue data in Autumn Semester 2023

Award: The Golden Owl
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
E-mailmarkus.kalisch@stat.math.ethz.ch
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.
401-3622-DRLStatistical Modelling Information Restricted registration - show details
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 Link with their name, course number and student ID. Please see Link
2 credits4GM. Kalisch
AbstractIn 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.
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
ContentIn 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 / NoticeThis 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).
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingassessed
Self-direction and Self-management fostered
401-3622-00LStatistical Modelling Information 7 credits4GM. Kalisch
AbstractIn 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.
ObjectiveIntroduction into theory and practice of a broad and popular area of statistics, from a modern viewpoint.
ContentIn 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 / NoticeThis 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).
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. 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
AbstractAbout 3 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 3 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.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Method-specific CompetenciesDecision-makingfostered
Problem-solvingfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
406-0603-AALStochastics (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 credits9RM. Kalisch
AbstractIntroduction 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.
ObjectiveThe 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".
ContentFrom "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/
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Personal CompetenciesSelf-direction and Self-management assessed