The registration deadline for enrollment in the course is Thursday in week 2 for the spring semester. You will receive confirmation of whether you received a spot in Studentweb no later than Tuesday the week after the deadline.
The time of the first lecture/orientation meeting can be found in the schedule on the course website or on the Mitt UiB learning platform.
Objectives and Content
The course introduces some statistical tools for regression analysis. It consists of lectures and computer based practicals, beginning with ordinary least squares and then developing other regression methods that allow the assumptions of ordinary least squared to be relaxed. The course is followed by a take home exam which covers both theoretical and practical aspects of the course.
After completing the course, students should be able to:
* Describe the estimator in ordinary least squares
* Explain the assumptions of ordinary least squares and the consequences of violating these assumptions
* Recognise when assumptions ordinary least squares are violated
* Choose appropriate regression technique given the properties ofthe data analysing data
* Interpret regression diagnostics and plots
* Build parsimonious models
* Make predictions with confidence intervals
* Analyses data in a modern statistical package
* Have some of the statistical skills necessary for their thesis projects
Forms of Assessment
Written take home exam on a given dataset. Graded.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Students will evaluate the course in accordance with the quality assurance system at UiB and the Department. You can find courseevaluations in the Quality Assurance Reports.
Type of assessment: Take-home examination
- Assignment handed out
- Submission deadline
- Withdrawal deadline