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Dette er studietilbudet for studieår 2019-2020. Endringer kan komme.


This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.

Learning outcome

After taking this course the student will:
  • be able to make and use statistical models for a number of problems in technology, natural science economy
  • have knowledge of the strengths and limitations of some key techniques for statistical modeling
  • be able to implement the models (in R)
  • carry out simulations of statistical models, analyze the results statistically, and be able to:
  • present results in a proper manner
  • make assessments of uncertainty in the results

Contents

The course focuses on methods to model and analyze a variety of random phenomena. The analysis will in practice often be done by simulation, but also the theoretical analysis is important. Students shall be able to implement statistical models on a computer, generate, interpret and present results. Topics that are appropriate to address: the general statistical model building, assessing the goodness of the model, estimation of distribution and parameters of the model and assess the uncertainty of estimates, bootstrap, number generators, variance reduction techniques, modeling and simulation of dependencies, modeling and simulation of stochastic processes. The course will have several exercises with the use of computers and the program R.

Required prerequisite knowledge

None.

Recommended prerequisites

MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1

Exam

Weight Duration Mark Supporting materials
Portfolio with 3 individual written assignments1/1 A - FAll.
The two first assignments counts 25% each, the last assigment counts 50%. Course teacher sets final marks.
Candidates who fail on the assignments will not be able to submit new project assignments until the next time the course is taught.

Course teacher(s)

Course coordinator
Stein Andreas Bethuelsen
Course teacher
Stein Andreas Bethuelsen , Jan Terje Kvaløy
Head of Department
Bjørn Henrik Auestad

Method of work

Six hours lectures and problem solving/data lab per week.

Overlapping courses

Course Reduction (credits)
Statistic modelling and simulation (TE6039_1) 5
Statistical modelling and simulation (MET260_1) 10

Open to

Mathematics and Physics - Bachelor's Degree Programme. Master studies at the Faculty of Science and Technology.

Course assessment

Usually by forms and/or discussion according to university regulations.

Literature

Maria L. Rizzeo, Statistical Computing with R, Second Edition, kap 1-10.


Dette er studietilbudet for studieår 2019-2020. Endringer kan komme.

Last updated: 15.12.2019