Discrete Models (DisMod)
Course content
Introduction to and analysis of a number of statistical models for discrete response variables: contingency tables, loglinear models, graphical models, smooth hypotheses in the multinomial distribution, Poisson regression, logistic regression and proportional-odds models, survey sampling.
MSc Programme in Statistics
Knowledge
At the end of the course the student will have knowledge about
different types of discrete models, the mathematical relationships
between them, and basic statistical properties of the models. The
student will have the knowledge to
* explain the asymptotic test theory for models for contingency
tables,
* explain the logistic regression model, in the fundamental version
for binary responses, as well as in the modifications for response
variables with several possible outcomes
* explain the theory for stratified survey sampling and multistage
survey sampling
* explain fundamental concepts within the theory of graphical
models
Skills
The student will acquire the skills to apply discrete models to
real data, decide on which model to use and which analysis to
perform. The student will have the skills to utilize theoretical
results in the practical analysis, including how complex models can
be specified by use of several covariates.
Competencies
At the end of the course the students will have the competence to
* carry out the analysis of 2-way and, more generally, k-way
contingency tables, theoretically as well as in practice.
* carry out practical analysis of simple graphical models.
* conduct the practical analysis of complex regression models with
response variables with a small number of possible outcomes.
* carry out practical survey analysis in simple situations.
4 hours of lecturing, 4 hours of exercise classes per week for 7 weeks
Mathematical Statistics or similar.
Academic qualifications equivalent to a BSc degree is
recommended.
Oral feedback will be given on students’ presentations in class. Brief feedback is given also on the mandatory assignment.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 3 days3 days written take-home assignment
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner for the ordinary exam.
Several internal examiners for the oral re-exam.
Criteria for exam assessment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 105
- Theory exercises
- 12
- Practical exercises
- 16
- Exam
- 45
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK11005U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Schedulegroup
-
B
- Capacity
- No limits
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Marie Leváková (4-6f636e67426f63766a306d7730666d)
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Courseinformation of students