Applied Statistics
5 credits
Syllabus, Bachelor's level, 1MS926
A revised version of the syllabus is available.
- Code
- 1MS926
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Mathematics G1F, Sociotechnical Systems G1F, Technology G1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 8 March 2016
- Responsible department
- Department of Mathematics
Entry requirements
Probability and Statistics.
Learning outcomes
In order to pass the course the student should be able to:
- use the most common statistical tests and understand their assumptions and limitations;
- formulate and choose a suitable methodology for testing in a given situation;
- use the most common estimation methods (e.g method of moments or the maximum-likelihood method);
- perform estimation in regression models and evaluate a proposed model;
- evaluate results from statistical software (e.g R).
Content
Statistical hypothesis testing (interpretation with confidence intervals, p-values), estimation methodology (ML and LS estimation), non-parametric methods, correlation analysis, multiple regression (estimation, prediction, diagnostics).
Instruction
Lectures, computer sessions. Guest lecture. Case studies where the course content is applied in problems arising in technology, the natural och social sciences.
Assessment
Written examination at the end of the course (4 credits) combined with assignments given during the course (1 hp).
Other directives
This course cannot be included in the same degree as the course 1MS026.