Statistics for Economists

MET2 Statistics for Economists

Spring 2021

  • Topics

    (i) Descriptive statistics

    (ii) Probability theory

    (iii) Combinatorics

    (iv) Conditional probability

    (v) Random variables, mean and variance. 

    (vi) Joint distributions

    (vii) Common probability distributions

    (viii) Estimation and estimators

    (ix) Hypothesis testing

    (x) Some particular hypothesis tests

    (xi) Linear regression in one and several variables

  • Learning outcome

    Upon successful completion the students can:


    • Understand statistical methods such that the students can analyze statistical data, and avoid the most common pitfalls in such analysis.


    • Interpret statistical data with the aid of central- and dispersion measures, frequency distributions and graphical methods.
    • Master basic probability theory, included is probability models, combinatorics, sampling models, conditional probability, the law of total probability, Bayes law and independence of random variables.
    • Analyze probability distributions and calculate expected value and variance of a random variable, and extend this to linear combinations of random variables.
    • Understand simultaneous probability distributions, included is calculation of expected value, variance and covariance.
    • Choose a probability model and do calculations with discrete and continuous probability distributions, included is the Binomial distribution, the Hypergeometric distribution, the Poisson distribution, the Normal distribution, approximation by normal distribution, and the t-distribution.
    • Estimate unknown parameters, included is point estimation and interval estimation.
    • Master hypothesis testing in sampling models and binomial models. Assess different methods for testing. Interpret levels of significance, P-values, and the strength of a test.
    • Assess the difference between two groups, included is hypothesis testing.
    • Use chi-square tests (test for models and test of independence)
    • Analyze covariation between two or more stochastic variables, by regression and by interpretation of the correlation coefficient, and by estimation and testing of the regression coefficient.

    General competence

    • Obtain theoretical insights into statistical analysis as a prerequisite to practical application of statistics in advanced courses.

  • Teaching


    • 18 plenary sessions
    • 2x2 hours repetitions the week before the exam

    Collaborative learning

    • 10 sessions of collaborative learning. The students are organized in mini-groups consisting of 4 persons and have 2 hours of such work each week. Mandatory participation in 6 out ot 10 sessions. 
    • 3  sessions are lead by the lecturer. 
    • 10 sets with problem sets for self-study. Students in need of support with these problems can seek help at oracle sessions. 
    • 1 set of voluntary assignments that can be commented by the teaching assistants.

    Multiple choice

    • 16 voluntary multiple choice tests, one for each plenary lecture. 


    • Student can seek help at oracle session every week throughout the semester. 


    It will be possible to follow the course digitally.

  • Requirements for course approval

    Course approval is given based on mandatory participation in 6 out of 10 sessions with collaborative learning. Of practical reasons the first session is mandatory. If students are unable to participate in the first session, they must inform the lecturer about this. 

  • Assessment

    Individual home exam (4 hours).

  • Grading Scale


  • Computer tools

    R: The contents is covered by a booklet which can be downloaded from Canvas.

  • Literature

    Alt 1: Ubøe. Statistikk for økonomifag, 4.utg., Gyldendal 2012

    Alt 2: Keller, Statistics for Management and Economics, 8th ed, Cengage Learning, 2009.


ECTS Credits
Teaching language

Spring. Offered Spring 2021.

Exam each semester.

Course responsible

Professor Jan Ubøe, Department of Business and Management Science.