Introduction
This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.
Content
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, basic Bayesian statistics and Markov chain Monte Carlo. The course will have several exercises with the use of computers and the program R.
Learning outcome
After completing this course the student will:
Knowledge
- be able to make and use statistical models for a number of problems in technology, natural science and economics
- have knowledge of the strengths and limitations of some key techniques for statistical modeling og simulation
Skills
- be able to implement the models (in R)
- carry out simulations of statistical models, analyze the results statistically, and
- be able to make assessments of uncertainty in the results
General competence
- be able to solve complicated problems using programming and computers
- present results in a proper manner
Required prerequisite knowledge
None
Recommended prerequisites
Mathematical Methods 1 (MAT100), Mathematical Methods 2 (MAT200), Probability and Statistics 1 (STA100)
Exam
Written exam
Weight 1/1
Duration 4 Hours
Marks Letter grades
Aid Standard calculator
Written exam is with pen and paper
The exam is given in English and may be completed in English or Norwegian.
Coursework requirements
Compulsory assignments
Two mandatory assignments must be approved in order to have access to the exam. Mandatory assignments may be completed in English or Norwegian.
Method of work
4 +2 hours of lectures and problem solving/data lab per week, self study. Language of instruction: English.
Open for
City and Regional Planning - Master
Computer Science - Master
Environmental Engineering - Master
Industrial Economics - Master
Structural and Mechanical Engineering - Master
Mathematics and Physics - Master
Mathematics and Physics - Master
Offshore Field Development Technology - Master of Science Degree Programme
Industrial Asset Management - Master
Marine and Offshore Technology - Master
Petroleum Engineering - Master
Exchange programme at The Faculty of Science and Technology
Admission requirements
Must meet the admission requirements of one of the study programmes the course is open for.
Course assessment
The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.
The course description is retrieved from FS (Felles studentsystem). Version 1