Modulbeschreibung MA4401

Modulbeschreibung

MA4401: Angewandte Regressionsanalyse

Fakultät für Mathematik

Modulniveau:
Master
Sprache:
Englisch
Semesterdauer:
Einsemestrig
Häufigkeit:
Wintersemester
Credits*:
5
Gesamt-
stunden:

150
Eigenstudiums-
stunden:

105
Präsenz-
stunden:

45
* Die Zahl der Credits kann in Einzelfällen studiengangsspezifisch variieren. Es gilt der im Transcript of Records oder Leistungsnachweis ausgewiesene Wert.
Beschreibung der Studien-/Prüfungsleistungen:
60 minute written exam with questions similar to those performed in the homework.
The 60-minute written exam will test their skills to accurately define all concepts in linear regression. The students prove selected theorems. They interpret R code and output in relation to linear regression. They choose appropriate hypothesis tests for univariate linear regression and perform them to assess the estimated parameters on real examples and give problem specific interpretations.
Wiederholungsmöglichkeit:
Im Folgesemester: Nein
Am Semesterende: Ja
(Empfohlene) Voraussetzungen:
Introductory statistics and probability course (e.g. MA0009), R statistical software
Angestrebte Lernergebnisse:
At the end of this course, students are able to
- solve real-world applications by identifying the appropriate regression modeling strategy to apply;
- diagnose problems with regression models, including failure of assumptions and outliers requiring removal;
- program and visualize effectively in the R statistical package
Inhalt:
Simple and multiple Normal linear regression, model diagnostics and selection procedures, survival regression, linear fixed and random effects models, logistic and Poisson regression, spatio-temporal models
Lehr- und Lernmethode:
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should animate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress. At the beginning of the module, the practice sessions will be offered under guidance, but during the term the sessions will become more independent, and intensify learning individually as well as in small groups.
Medienformen:
Lecture slides, exercise sheets, R programming
Literatur:
Abraham B, Ledolter J. Introduction to Regression Modeling. Thomson/Brooks Cole, 2006.
Modulverantwortliche(r):
Ankerst, Donna; Prof. Ph.D.: ankerst@tum.de
Lehrveranstaltungen (Lehrform, SWS) Dozent(in):

0000000386 Applied Regression [MA4401] (2SWS VO, WS 2020/21)
Ankerst D, Miller G

0000000389 Exercises for Applied Regression [MA4401] (1SWS UE, WS 2020/21)
Ankerst D, Miller G