EBA 2904 Statistics with Programming

EBA 2904 Statistics with Programming

Course code: 
EBA 2904
Course coordinator: 
Steffen Grønneberg
Course name in Norwegian: 
Statistics with Programming
Product category: 
Bachelor - Common Courses
2022 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course gives an introduction to statistics. Main themes are descriptive statistics, probability and hypothesis testing. Formulas and formal procedures play an important role, but the emphasis is on developing statistical literacy and critical thinking.

Learning outcomes - Knowledge

After compledted course students shall:

  • Understand the relevance of statistical analysis in economics and marketing
  • Acuire knowledge of basic concepts and overview of statistics
  • Understand the difference between a population and a sample
  • Understand what a random variable and a statistical model is
  • Understand that statistical methods are based on assumptions that must be checked
  • Understand what estimation and hypothesis testing is about
  • Be acquainted with the statistical and mathematical notations used in statistics
Learning outcomes - Skills

After completed course students will be able to:

  • Discern randomness from real underlying effects
  • Use statistical software and interpret computer displays from such software
  • Produce statistical graphics
  • Compute probabilities and confidence intervals
  • Conduct the most basic types of hypothesis testing and estimation
  • Reflect on the role of randomness
General Competence

Students will understand that in many situations a statistical analysis will help making better decisions, but also be ware that statistical methods can be wrongly applied and lead to false conclusions.

Course content
  • Probability. Basic definitions. Limits of relative frequencies.
  • Introduction to Monte Carlo simulation.
  • Random variables and their distributions. Approximating distributions through Monte Carlo simulations: Histograms as approximations to densities.
  • The law of large numbers and the central limit theorem illustrated through Monte Carlo simulations.
  • Confidence intervals and hypothesis testing in large samples. A brief summary of the t-test under normality assumptions.
  • Correlation and simple linear regression.
  • Assessing the performance of statistical procedures using Monte Carlo simulations.
Teaching and learning activities

There are 48 course hours, some of which will be used for instructions in the computer software Python.

Work requirements
There are 8 mandatory multiple choice tests to be answered in It's Learning. 

If a test is not approved in the first try, the student can retry. It is mandatory that the student get approved at least 5 of these tests in order to take the final exam.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

Coursework requirements was waived spring 2022. Consequently, Coursework requirements does not apply for students taking re-sit exam autumn 2022. 


Higher Education Entrance Qualification


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.


Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

No particular prerequisites.

Mandatory courseworkCourseworks givenCourseworks requiredComment coursework
Mandatory85The student must pass 5 of the 8 Work Requirements to be able to sit for the exam. 19.09.2022 Coursework requirements was waived spring 2022. See Additional information below.
Mandatory coursework:
Mandatory coursework:Mandatory
Courseworks given:8
Courseworks required:5
Comment coursework:The student must pass 5 of the 8 Work Requirements to be able to sit for the exam. 19.09.2022 Coursework requirements was waived spring 2022. See Additional information below.
Exam category: 
Form of assessment: 
Written submission
Support materials: 
  • Bilingual dictionary
5 Hour(s)
Exam code: 
EBA 29041
Grading scale: 
Examination every semester
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
48 Hour(s)
50 Hour(s)
Group work / Assignments
42 Hour(s)
Student's own work with learning resources
57 Hour(s)
3 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.