Generalized Linear Models

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Generalized Linear Models - Course Details

Delve into the course contents and find out about the faculty members.

Single Course Price:

800.00 EUR (tax exempt)

 

Instructor:

Prof. Thomas Gautschi (University of Mannheim)

Video lecture:

Prof. Thomas Gautschi (University of Mannheim)

 

Course Dates

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Book this course here!

In order to book the course with alumni conditions, please get in touch with Manon Pfeifer directly.

Course Description

Short Course Description
The main focus of this course lies on the introduction to statistical models and estimators beyond linear regression useful to social and economic scientists. It provides an overview of generalized linear models (GLM) that encompass non-normal response distributions to model functions of the mean. GLMs thus relate the expected mean E(Y) of the dependent variable to the predictor variables via a specific link function. This link function permits the expected mean to be non-linearly related to the predictor variables. Examples for GLMs are the logistic regression, regressions for ordinal data, or regression models for count data. GLMs are generally estimated by use of maximum likelihood estimation. The course thus not only introduces GLMs but starts with an introduction to the principle of maximum likelihood estimation. A good understanding of the classical linear regression model is a prerequisite and required for the course.

Prerequisites
A sound understanding of linear regression models (OLS) is required. Knowledge in linear algebra and calculus is useful.

Course Objectives
By the end of the course, students will…

  • understand how to appropriately translate research questions into statistical models
  • be able to apply statistical models appropriate for non-linear problems
  • estimate regression parameters using the maximum likelihood principle
  • perform hypothesis tests for regression models using the maximum likelihood principle
  • be able to identify limitations of non-linear regression models
  • be able to identify violations of the respective regression assumptions of the discussed GLMs

Course Composition
This is a 4 ECTS course, which runs for 8 weeks. The content of the course is broken down into 4 units:

  1. Maximum Likelihood Estimation
  2. Binary Choice Models
  3. Models for Ordinal Data
  4. Models for Count Data

Learning and Teaching Methods
In this course, you are responsible for watching video recorded lectures and reading the required literature for each unit and then “attending” mandatory weekly one-hour online meetings where students have the chance to discuss the materials from a unit with the instructor. In addition, students are encouraged to post questions about the materials covered in the videos and readings of the week in the forum before the meetings. Just like in an on-site course, homework will be assigned and graded and there will be a final exam at the end of the course.

Grading
Grading will be based on:

  • 7 homework assignments (worth 49% total)
  • Participation in online meetings and submission of questions demonstrating understanding of readings (worth 10%)
  • Final Exam (worth 41%)

 

ZFU Certification and Online Dispute Resolution

ZFU Certification

The Mannheim Master of Applied Data Science & Measurement program is certified according to the regulations of the ZFU (Staatliche Zentralstelle für Fernunterricht).

 

Online Dispute Resolution

Online dispute resolution according to Art. 14 Sect. 1 ODR-VO: The European Commission provides a platform for online dispute resolution (ODR). You can find more information under http://ec.europa.eu/consumers/odr/

MANNHEIM BUSINESS SCHOOL (MBS)

Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.

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