ME-201 Business Analytics 2
- ECTS Credits:
- 7.5
- Responsible department:
- School of Business and Law
- Course Leader:
- Eirin Mølland
- Lecture Semester:
- Spring
- Teaching language:
- English
- Duration:
- 1 term
The course is connected to the following study programs
- Bachelor's Programme in Business Administration
- Master's Programme in Business Administration (5 years)
Teaching language
EnglishPrerequisites
Business Analytics 1 or equivalent
Course contents
The main focus of Business Analytics 2 is to give students a thorough introduction to statistical analysis techniques used in econometrics. In the course, special emphasis will be placed on linear regression analysis and its extensions. Students will learn how the methods taught can be applied to analyze data using statistical program (STATA). The course builds on Business Analytics 1.
Learning outcomes
After completing this course, students should be able to:
-
Describe the general research process in a quantitative research project
-
Understand which research questions can be answered using quantitative method
-
Have knowledge about and understand the assumptions that linear regression analyzes are based on
-
Be able to perform and interpret linear regression analyzes using appropriate software
-
Understand the difference between correlation and causality
-
Be able to interpret and critically evaluate empirical research.
Examination requirements
Approved compulsory assignments. More information will be provided at the start of the semester.
Teaching methods
Lectures, groupwork and DataLab (STATA). Estimated workload is about 200 hours.
Evaluation
The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.
Admission for external candidates
No
Assessment methods and criteria
3 hour written examination. Graded by letters.