Kun tilgjengelig på engelsk

EXC 2904 Statistics

EXC 2904 Statistics

Course code: 
EXC 2904
Course coordinator: 
Einar Christopher Wellén
Course name in Norwegian: 
Product category: 
Bachelor - Common Courses
2024 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This is a basic statistics course that is included as a mandatory part of the bachelor programs in business administration subjects. The course provides an introduction to basic probability theory, some statistical methods and elementary use of statistical data tools in order to be able to effectively process and analyze data. Use of statistical software is an important element of the course.

Learning outcomes - Knowledge

After completing the course, the student should have;

  • Acquired knowledge within basic probability theory, including knowledge of certain probability distributions and the central limit theorem and why this knowledge is necessary to justify the use of various statistical methods to analyze data.
  • Acquired knowledge of various statistical methods for analyzing data.
  • Acquired knowledge of the elementary use of statistical data tools and have understood that the use of statistical data tools is necessary to be able to effectively process and analyze data.
Learning outcomes - Skills

After completed course students shall:

  • Be able to plan and carry out investigations using the most commonly used statistical methods.
  • Be able to interpret analysis results from, for example, reports or computer printouts.
  • Be familiar with being able to use computer tools to process and analyze data.
General Competence
  • The students' ability for analytical thinking and an ability to reflect on results and calculations should be strengthened by completing the course.
  • To understand that statistical methods can easily be misused.
  • Taking anonymity and privacy into account when collecting data.
  • To know that there are other branches within the statistics subject, such as econometrics and machine learning.
Course content
  • Descriptive statistics: Central measures, dispersion measures, covariation measures and graphical representations.
  • Combinatorics and probability calculus. Conditional probability.
  • Random variables. Expectation, variance and covariance.
  • Central probability distributions with emphasis on the indicator distribution, binomial distribution, the normal distribution and the t-distribution. We also look at a general simultaneous distribution.
  • Estimators and estimation. Sampling distributions and the central limit theorem. Confidence intervals.
  • Hypothesis tests for, among other things, average, proportion, comparison of averages in two groups, comparison of proportions in two groups, chi-square test for probabilities, chi-square test for covariation between categorical variables.
  • Simple linear regression including inference (confidence intervals and hypothesis tests for the regression parameters).
  • Active use of statistical software.
Teaching and learning activities

The course is carried out with 54 course hours which will consist of ordinary lectures where the syllabus is reviewed and problem solving (including problems which must be solved with computer tools). The software R is integrated into the teaching and the students will, through task solving, also actively process and analyze data using statistical software on their own.

Solving problems will be a central part of the joint lectures where the students are presented with tasks in the lecture and receive feedback by solving, reviewing and discussing these.

For each theme, a work program will be drawn up with literature references and task sets. The student must acquire the material in the literature reference and solve the tasks.

Computer tools are used in the subject. The tool will be R or other suitable software. The software R is free to download and does not require a license.

In course delivery as online courses, lecturer will, in collaboration with the student administration, organize an appropriate course implementation, combining different learning activities and digital elements on the learning platform. Online students are also offered a study guide that will contribute to progression and overview. Total recommended time spent for completing the course also applies here.

Software tools
Additional information

Students who do not pass the written exam, or who wish to improve their grade, can take a new continuation exam when the exam is completed later.


Higher Education Entrance Qualification


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

No particular prerequisites.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
Grading scale:
Grading rules:
Internal examiner
Examination when next scheduled course
100Yes5 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:5 Hour(s)
Exam code:EXC29041
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
54 Hour(s)
Prepare for teaching
66 Hour(s)
Group work / Assignments
75 Hour(s)
5 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.