FORK 1002 Preparatory Course in Statistics


FORK 1002 Preparatory Course in Statistics

Responsible for the course
Genaro Sucarrat

Department of Economics

According to study plan

ECTS Credits

Language of instruction

This course focuses on statistical concepts and tools of relevance in business and managerial economics in particular, and in the social sciences more generally.

Learning outcome
To provide students with the understanding of the fundamentals of basic statistical principles (the skills necessary for interpretation and evaluation of data) sufficient knowledge for the adequate application of basic statistical procedures.


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
Groebner, David F., Patrick W. Shannon, Phillip C. Fry. 2014. Business statistics : a decision-making approach. 9th ed., International ed. Pearson. Selected chapters

Excercises, selected readings and hand-outs during the course

Recommended reading

Course outline
Key Concepts and Basic Statistics (4 hour)

  • Key Concepts
  • Descriptive statistics
  • Frequency and Probability Distributions
  • Hypothesis Testing
  • P-Values
  • Interval Estimation
Regression Analysis (8 hours)
  • Bivariate correlation analysis
  • The linear regression model
  • Estimation
  • Hypothesis testing

Qualitative independent variables (3 hours)
  • Qualitative vs. Quantitative variables
  • Dummy variables
  • Combining qualitative and Quantitative variables

Computer-based tools

Learning process and workload
15 hours with lectures and 5 hours for the use of statistical software.

Practical examples and assignments will involve extensive use of statistical software, such as SPSS or STATA.

Not applicable

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.