MET 1190 Statistics

MET 1190 Statistics

Course code: 
MET 1190
Course coordinator: 
Christian Brinch
Course name in Norwegian: 
Product category: 
Bachelor - Common Courses
2018 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course gives an introduction to basic probability and basic classic statistics. Probability is a tool used in many contexts that student will encounter later in their studies and working lives. Methods from probability are used to analyze risk and uncertainty in finance and economics. In this course, the probability is used for statistics. The course gives a short introduction to basic descriptive statistics. In addition, a thorough introduction is given to classic statistical inference, methods that are used to describe uncertainty in the conclusions from statistical analyses. As part of the classical statistics, an introduction to regression analysis is also included in the course.

Learning outcomes - Knowledge

After completing the learning process described in this course description, students will:

  • Know basic terms from probability, like «random variabel», «expectation», «variance», «probability distribution», «statistical independence» and «conditional probability.
  • Know basic models for discrete and continuous random variables, like the normal distribution, the binomial distribution and the Poisson distribution.
  • Know basic terms from classic statistics, like «estimator», «null hypothesis», «two-sided test», «p-value» and «confidence interval».
  • Know basic methods for describing the relationship between two variables, like «scatter plot», «covariance» and «regression analysis».
Learning outcomes - Skills

After completing the learning process described in this course description, students will:

  • Be able to apply standard methods, graphical and tabular, for describing a sample or dataset.
  • Be able to apply basic rules from probabiity for solving simple problems, in particular related to card games, dice games, draws from small samples etc.
  • Be able to perform one-sided and two-sided hypothesis tests for hypothesis about the population mean when the populastion variance is known or unknown, and in addition the case where we study a population share – based on random samples.
  • Be able to construct confidence intervals for population means, population shares and population variances based on random samples.
  • Be able to perform simple regression analyses to describe the relationship between two variables, including hypothesis tests or confidence intervals.
Learning Outcome - Reflection

At the end of the course, the students will have aquired an understanding of the importance of why and how we reach different techniques and formulas in statistics, in addition to being able to apply the formulas.

Course content
  • Probability calculus
  • Random variables
  • Standard probability models
  • Descriptive statistics
  • Estimation and hypothesis testing
  • Analysis of the relationship between variables
Learning process and requirements to students

To each lecture there will be exercises and reading assignments. The student must gain knowledge from the material presented in the reading assignments and work through the exercises. The exam will require that the student has solved the exercises during the semester. Feedback will be given by sample solutions and presentations.

Software tools
No specified computer-based tools are required.

Higher Education Entrance Qualification.

Required prerequisite knowledge

Basic skills in mathematics and statistics equivalent to admission requirements for the program.

Exam category: 
Form of assessment: 
Written submission
Support materials: 
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
5 Hour(s)
Exam code: 
Grading scale: 
Examination every semester
Exam organisation: 
Ordinary examination
Total weight: 
Student workload
54 Hour(s)
Group work / Assignments
96 Hour(s)
Student's own work with learning resources
40 Hour(s)
10 Hour(s)
Exam incl. preparation
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.