Matteo Tanadini: Katalogdaten im Herbstsemester 2019 |
| Name | Herr Dr. Matteo Tanadini |
| Adresse | Seminar für Statistik (SfS) ETH Zürich, HG G 14.1 Rämistrasse 101 8092 Zürich SWITZERLAND |
| matteo.tanadini@math.ethz.ch | |
| Departement | Mathematik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 401-0629-00L | Applied Biostatistics | 4 KP | 3G | M. Tanadini | |
| Kurzbeschreibung | This course covers the main methods used in Biostatistics. It starts by revising Linear Models (Regression, Anova), then moves to Generalised Linear Models (logistic regression and methods for count data) and finally introduces more advanced topics (Linear Mixed-Effects Models and Survival Analysis). The course strongly focuses on applied aspects of data analysis. | ||||
| Lernziel | After this course students: - revised Linear Models - revised or got introduced to Generalised Linear Models - got introduced to Linear Mixed-Effects Models and Survival Analysis - are able to select among these methods to solve an applied problem in Biostatistics - can perform the analysis using the statistical software R - can interpret the results of such an analysis and draw valid "biological" conclusions | ||||
| Inhalt | This course is structured into three parts. The first part focuses on Linear and Generalised Linear Models. The second part introduces more advanced methodologies such as Linear Mixed-Effects Models and Survival Analysis. Both, part one and two will included the following topics: exploratory data analysis, model fitting, model "selection", residual diagnostics, model validation and results interpretation. Analyses will be carried out by using the statistical software R. Finally, in the third part of the course students will be analysing real-world data sets to put into practice the knowledge and skills acquired during the first two parts. | ||||
| Voraussetzungen / Besonderes | The statistical software R will be used in the exercises. If you are unfamiliar with R, it is highly recommend to view the online R course etutoR. | ||||
| 447-0649-01L | Angewandte statistische Regression I  Nur für DAS und CAS in Angewandter Statistik. | 4 KP | 1V + 1U | M. Tanadini | |
| Kurzbeschreibung | Einfache und multiple lineare Regression. Praktische Aspekte bei der Durchführung und Interpretation. Residuenanalyse und Modellwahl. | ||||
| Lernziel | Verständnis des Modells der multiplen linearen Regression und seiner grundlegenden Bedeutung für die Modellierung und Vorhersage. Durchführung von Regressionsanalysen mit der Statistiksoftware R und korrekte Interpretation von Resultaten. Modellkritik mit Residuenanalyse. Strategien der Modellwahl. | ||||