*Warning! The directory is not yet complete and will be amended until the beginning of the term.*

# 230150 UE EC: Logistic Regression (2019S)

Continuous assessment of course work

## Labels

## Registration/Deregistration

**Note:**The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

- Registration is open from
**Mo 04.02.2019 00:01**to**Th 21.02.2019 10:00** - Registration is open from
**Mo 25.02.2019 10:00**to**Tu 26.02.2019 10:00** - Deregistration possible until
**We 20.03.2019 23:59**

## Details

max. 40 participants

Language: English

### Lecturers

### Classes (iCal) - next class is marked with N

Monday
18.03.
09:00 - 13:00
PC-Raum 1 Schenkenstraße 8-10, 1.UG

Monday
25.03.
09:00 - 13:00
PC-Raum 1 Schenkenstraße 8-10, 1.UG

Monday
01.04.
09:00 - 13:00
PC-Raum 1 Schenkenstraße 8-10, 1.UG

## Information

### Aims, contents and method of the course

### Assessment and permitted materials

The format of classes will be informal. Lectures will be short, and the focus of classes will be computer exercises and classroom discussions of results and homework. Lectures will take up at most a third of the overall classroom time as the focus in this class is on practical analysis. Students are encouraged to bring along their own data and research questions.Hinweis der SPL: bei Feststellung einer erschlichenen Teilleistung (Abschreiben, Plagiieren, Ghostwriting, etc.) muss die gesamte Lehrveranstaltung als geschummelt gewertet und als Antritt gezählt werden.

### Minimum requirements and assessment criteria

There will be two homework assignments (30 % each) as well as a final assignment (30 %) that will help participants to gain further understanding and experience in interpreting binary logistic regression models. Participation will account for 10% of the grade. Attendance at all classes is compulsory, though half of one class can be missed.

### Examination topics

### Reading list

## Association in the course directory

*Last modified: Mo 07.09.2020 15:39*

- interpret the results of binary logistic regression models using log odds, odds ratios and predicted probabilities,

- present these results as tables and graphs in ways suitable for general and specialist audiences,

- interpret interaction effects in the appropriate ways,

- use simulations to create measures of uncertainty for the predicted effects,

- distinguish different measures of model fit and include these in presentations of results,

- run straightforward diagnostic tests of their model,

- and use Stata to run and understand binary logistic regression models.