FORK 1005 Preparatory course in Mathematics for Multivariate Statistics, MSc
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APPLIES TO ACADEMIC YEAR 2016/2017 |
FORK 1005 Preparatory course in Mathematics for Multivariate Statistics, MSc
Responsible for the course
Steffen Grønneberg
Department
Department of Economics
Term
According to study plan
ECTS Credits
0
Language of instruction
English
Introduction
We review central mathematical concepts that are especially relevant for multivariate statistics.
At the start of fall-semesters, the course will be given for students in the following programs :
- Msc in Leadership and Organizational Psychology
- Msc in Strategic Marketing Management
At the start of spring-semesters, the course will be given for students taking a MSc in Business with the following specializations:
- Business Law, Tax and Accounting
- HRM
- International Business
- Logistics - Supply Chain and Network
- Marketing
- Strategy
This course is not relevant for students doing MSc in Finance or MSc in Business, major Finance or Economics.
Learning outcome
To provide the basic mathematical tools for multivariate statistics courses to students who need to refresh their mathematical skills.
Prerequisites
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Compulsory reading
Other:
Reading material will be distributed on It`s learning before the course starts.
Recommended reading
Course outline
The course reviews the mathematical theory surrounding applied statistics at the level of multiple linear regression. This includes calculation rules with the summation notation, standardization of random variables and its use for Normally distributed random variables, and related mathematical issues.
Computer-based tools
Not applicable
Learning process and workload
There will be 10 teaching hours in the course.
Examination
| Form of assessment | Weight | Group size |
| Not applicable | ||
Examination code(s)
Not applicable
Examination support materials
Re-sit examination
Not applicable
Additional information
Honour code. Academic honesty and trust are important to all of us as individuals, and are values that are integral to BI's honour code system. Students are responsible for familiarising themselves with the honour code system, to which the faculty is deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for academic misconduct. Issues of academic integrity are taken seriously by everyone associated with the programmes at BI and are at the heart of the honour code. If you have any questions about your responsibilities under the honour code, please ask. The learning platform itslearning is used in the teaching of all courses at BI. All students are expected to make use of itslearning.