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Before choosing courseIX1501 Mathematical Statistics 7.5 creditsAdministrate About course

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* Retrieved from Course syllabus IX1501 (Autumn 2019–)

Content and learning outcomes

Course contents

Probability theory: probability, conditional probability, independenceone-dimensional random variablesbriefing about multi-dimensional random variablescommon distributionsmeasures (location, spreading and dependence) Law of Large Numbers, Central Limit Theorem Statistics: point estimates, confidence intervalshypothesis testregression analysis, correlation, graphical presentation of data.

Intended learning outcomes

General Objectives

After course completion the student should be able to:

  • formulate, analyze and solve problems in statistics significant to in the ICT sphere.
  • apply and develop statistical models with the aid of mathematical programming language.
  • review and comment a given solution to a problem.
  • comment domain and propose improvements to a statistical model.
  • make presentations of solutions of a statistical problem.

Detailed Objectives

After course completion the student should be able to:

  • apply basic stochastic models and use these to determine summary measures and probabilies.
  • use normal approximation according to CLT.
  • apply basic statistical models to an experiment.
  • specify a standard model and comment the fitness for given data.
  • describe data with summary measures, such as mean, variance and covariance.
  • present data graphically in a suitable way.
  • compute point estimates and confidence intervals.
  • estimate error risks in hypothesis testing.
  • compute correlation and regression line.

Course Disposition

The teaching method is problem oriented and computer aided. The education time is evenly distributed among the three main topics

  • conceptual understanding and modelling
  • algorithms
  • conclusions and synthesis

Literature and preparations

Specific prerequisites

Entrance qualifications:

  • IX1303 - Algebra och Geometry
  • IX1304 - Calculus

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • INLA - Assignment, 3,5 hp, betygsskala: P, F
  • TENA - Written exam, 4,0 hp, betygsskala: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The examiner decides, in consultation with KTH's coordinator for disabilities (Funka), about possible adapted examination for students with documented, permanent disabilities. The examiner may permit other examination format for re-examination of individual students.

Other requirements for final grade

  • Written exam (TEN1; 3,5 credits)
  • Problem assignments (INL1; 4,0 credits)

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Profile picture Håkan Olsson

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web IX1501

Offered by

EECS/Computer Science

Main field of study

Mathematics, Technology

Education cycle

First cycle

Add-on studies

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Ki Won Sung (

Supplementary information

In this course, the EECS code of honor applies, see: