Main Field of Study and progress level:
Statistics: Second cycle, has only first-cycle course/s as entry requirements
Grading scale: Pass with distinction, Pass, Fail
Responsible department: Department of Statistics
Contents
This course covers linear and generalized linear models, mixed effects models and non-parametric regression models applied to data from different experimental an non-experimental (observational) studies. The statistical computing environment R is used thoroughly for statistical analysis. Particular emphasis is placed on the interplay between method of data collection and choice of statistical model, i.e. how the way data are collected (experiment, observation) will affect the choice of model for analysis and vice versa.
Expected learning outcomes
After completion of the course, the student is expected to be able to:
- plan a data collection on the basis of an assumed statistical model for the analysis of data,
- evaluate the potential usefulness of the considered models in different situations of data collection,
- analyse data using an appropriate model,
- perform data analysis using the statistical computing environment R,
- report the results of a statistical analysis, both in writing and orally,
- discuss the implications of the results of a statistical analysis.
Required Knowledge
At least 75 academic credits in statistics and/or mathematical statistics, or equivalent. English course B at Swedish gymnasium or equivalent.
Form of instruction
The course is given as lectures, seminars and computer assignments. To a great extent, the course is based on the students own work with the course literature and active participation in the lectures, seminars and computer assignments.
Examination modes
The examination consists of:
- a written and/or oral examination at the end of the course
- a number of compulsory assignments to be completed, documented and presented orally.
Literature
Valid from:
2010 week 3
Faraway Julian J. Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models
