Text

Course syllabus - Probability Theory and Statistical Inference

Scope

7.5 credits

Course code

MAA137

Valid from

Autumn semester 2026

Education level

First cycle

Progressive Specialisation

G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements)

Main area(s)

Mathematics/Applied Mathematics

Organisation

Department of Business and Mathematics

Ratified

2014-12-12

Revised

2025-11-03

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2025-12-02
    • Latest update: 2026-01-12

    Books

    The following book applies when the course is given in English:

    Wackerly, Dennis D.; Mendenhall, William; Scheaffer, Richard L.

    Mathematical statistics with applications

    ISBN: 9780495385080

    LIBRIS-ID: 10617209

    7. ed., Southbank, Thomson Learning, xxii, 912 s.

    Books

    The following book applies when the course is given in Swedish:

    Alm, Sven Erick; Britton, Tom

    Stokastik: sannolikhetsteori och statistikteori med tillämpningar

    ISBN: 9789147053513

    LIBRIS-ID: 10820212

    1. uppl., Stockholm, Liber, viii, 530 s.

    • Ratified date: 2025-07-09
    • Latest update: 2025-07-09

    Books

    Wackerly, Dennis D.; Mendenhall, William; Scheaffer, Richard L.

    Mathematical statistics with applications

    ISBN: 9780495385080

    LIBRIS-ID: 10617209

    7. ed., Southbank, Thomson Learning, xxii, 912 s.

    Reference Literature

Objectives

The course presents basic concepts and methods of probability theory and statistical inference, and provides increased skills in application of these disciplines in science, technology and economics.

Learning outcomes

  1. formulate and apply basic concepts and laws for probability such as sample point, event and sample space; the probability definition, independent events and conditional probability; basic properties of probability, the Law of Total Probability and Bayes' rule.
  2. formulate and apply basic properties and functions of random one-dimensional variables.
  3. formulate definition of and apply following distributions: the geometric distribution, the uniform distribution, the binomial distribution, the Poisson distribution, the exponential distribution and the normal distribution.
  4. describe and apply measure of central tendency, dispersion and dependency.
  5. describe and apply the main methods and concepts of point estimation, confidence intervals estimation, hypotheses testing and linear regression models.

Course content

  • Probability: Basic concepts. Discrete and continuous random variables. Functions of random variables. One-dimensional distributions. Measure of central tendency, dispersion and dependency. Geometric distribution, binomial distribution, hypergeometric distribution, Poisson distribution, uniform distribution, exponential distribution and normal distribution. Approximations of the binomial and Poisson distribution. The Central Limit Theorem.
  • Statistical Inference:Estimation. The Method of Least Squares. The Method of Maximum Likelihood. Confidence Intervals. Hypothesis Testing (incl. Chi-square test). Linear Regression.

Specific requirements

Basic Calculus, 7.5 credits, of which 4 credits must be completed at the beginning of the course, or equivalent.

Examination

TEN1, Examination, 3,5 credits, individual written and/or oral examination regarding learning outcomes 1-3, marks, Fail ( F), 3, 4 or 5.
TEN2, Examination, 4 credits, individual written and/or oral examination regarding learning outcomes 1-5, marks Fail (F), 3, 4 or 5.

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Grading scale: 5, 4, 3