ST813: Statistical Modelling (10 ECTS)

STADS: 25003801

Level
Master's level course

Teaching period
The course is offered in the spring semester.

Teacher responsible
Email: colchero@imada.sdu.dk

Timetable
Show entire timetable
Show personal time table for this course.

Comment:
Ubegrænset deltagerantal. Fælles undervisning med ST523 Statistisk modellering

Prerequisites:
None

Academic preconditions:
Students taking the course are expected to:

• Have knowledge of linear algebra, calculus, basic statistics
• Be able to use the statistical software R 

Course introduction
The aim of the course is to enable the student to gain insight into the mathematical structure of linear and generalized linear models, including experience in recognizing such models from a given statistical problem.

The course builds on the knowledge acquired in the courses ST521: Mathematical Statistics and on knowledge of linear algebra corresponding to the course MM538: Algebra and Linear Algebra, and gives an academic basis for advanced courses in statistics and master thesis projects.

In relation to the competence profile of the degree it is the explicit focus of the course to:

• have an overview of the various types of linear and generalized linear models and the main examples of these, as well as to identify which problems can be solved by means of such models;
• be skilled at manipulating the mathematical and statistical elements of linear and generalized linear models and to clearly distinguish between exact and asymptotic results;
• know how to securely apply the theoretical results for linear and generalized linear models to concrete examples and explain the practical interpretation of the results
• have familiarity with the statistical package R, and routine in its use for statistical modeling.

Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:

• recognize the different types of statistical models and describe their similarities and differences, and explain the role that the response variable, explanatory variables, variance function and link function play for statistical modeling;
• be able to manipulate the mathematical and statistical elements of linear and generalized linear models, such as parameters and principles of estimation, the derivation of statistical tests based on standard errors deviance and residual sum of squares;
• obtain an overview of the most important examples of linear and generalized linear models, and to derive theoretical properties of new models based on the general theory;
• recognize the importance of and the difference between regression and dispersion parameters, and use this knowledge in practical and theoretical contexts:
• carry out practical data analysis using statistical modeling, including investigation of a model’s adequacy using residual analysis;
• perform the statistical analysis using the statistical software R, including the ability to identify and interpret relevant information in the program output;
• document the results of a statistical analysis in the form of a written report.

Subject overview


Literature

Meddeles ved kursets start.

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
None

Assessment and marking:

1. First take home exam, (5 ECTS). 7-mark scale, external marking. (25003802).
2. Second take home exam, (5 ECTS). 7-mark scale, external marking. (25003812).

Expected working hours
The teaching method is based on three phase model.
Intro phase: 48 hours
Skills training phase: 32 hours, hereof:
 - Tutorials: 32 hours

Educational activities
The students are expected to:

• Work with the new concepts and terms introduced.
• Increase their understanding of the topics covered during the lectures.
• Solve relevant exercises.
• Read the text book chapters and the scientific journal articles provided as support for the lectures

Educational form
In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours or the training phase hours.

Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.