Markus Kalisch: Catalogue data in Spring Semester 2019

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 or 044 632 34 30

Requirements: Knowledge of the basic concepts of statistics is desirable.
401-0643-00LStatistics I Information 3 credits2V + 1UM. 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.
ObjectiveGrundverständnis 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.
ContentModelle 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 notesEs steht ein kurzes Skript zur Verfügung.
Literature- W. A. Stahel, Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, 5. Aufl., Vieweg, Braunschweig/Wiesbaden, 2007
Prerequisites / NoticeVoraussetzungen: Grundlegende Mathematik-Kenntnisse wie sie im ersten Semester erworben werden.
401-4620-00LStatistics Lab Restricted registration - show details
Number of participants limited to 27.
6 credits2SM. Kalisch, M. H. Maathuis, M. Mächler, 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.
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.
ContentStudents 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 notesn/a
LiteratureThe required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.
Prerequisites / NoticePrerequisites:
Sound knowledge in basic statistical methods, especially regression and, if
possible, analysis of variance. Basic experience in Data Analysis with R.
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. 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
Abstract5 to 6 talks on applied statistics.
ObjectiveKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
ContentIn 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 notesBei 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 / NoticeDies 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-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. 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.
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". The main topics of the course are:
- Introduction to probability
- Common distributions
- Binomialtest
- z-Test, t-Test
- Regression
ContentFrom "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/
701-0105-00LMathematics VI: Applied Statistics for Environmental Sciences
This course is mandatory for students for whom the study regulation 2016 is binding.

Requirement: enrollment of 401-0624-00L Mathematics IV: Statistics or similar lecture.

This course will be offered in autumn semester 2018 (for students who started in autumn 2016) and in spring semester 2019 (for students who started in autumn semester 2017).

This course will be offered in spring semester only starting with the study year 2019/20.
3 credits2GC. Bigler, M. Kalisch, L. Meier
AbstractStatistical 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.
ObjectiveStudents 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
ContentStatistische 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 / NoticeBesuch von "Mathematik IV: Statistik" oder vergleichbare Lehrveranstaltung.
Die Schlussprüfung findet am Freitag 14.6.2019 (9:00 - 10:30) statt.