Probability Theory, Stochastic Processes and Applied Statistics

2018/2019

Content, progress and pedagogy of the module

Learning objectives

Knowledge

  • Have knowledge about fundamental concepts in probability, including conditional probability and independence
  • Have knowledge about discrete and continuous random variables and relevant properties of these
  • Have knowledge about various examples of descriptive statistics and graphics, e.g. histograms, boxplots, scatterplots, lag plots and auto covariance plots
  • Have knowledge about statistical inference, including estimation, confidence intervals and hypothesis testing
  • Have knowledge about basic concepts related to stochastic processes such as stationarity, correlation function and spectral density
  • Have elementary knowledge about wiener processes, white noise and linear stochastic differential equations
  • Have comprehension of a concrete example of a model for a simple stochastic process

Skills

  • Be able, given specific data, to specify a relevant statistical model and account for the assumptions and limitations of the chosen model
  • Be able to use relevant software for carrying out the statistical analysis of given data and be able to interpret the results of the analysis
  • Be able to use statistical models, like linear regression (simple and multiple) and analysis of variance

Competences

  • Be able to judge the applicability of statistics within own area
  • Be capable of performing a critical evaluation of the results of a statistical analysis
  • Be capable of communicating the results of a statistical analysis to people with no or little background within statistics.

Type of instruction

Lectures in combination with practical exercises and self-study or similar.



Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.

Exam

Exams

Name of examProbability Theory, Stochastic Processes and Applied Statistics
Type of exam
Written or oral exam
ECTS5
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentAs stated in the Joint Programme Regulations.
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Facts about the module

Danish titleSandsynlighedsregning, stokastiske processer og anvendt statistik
Module codeN-EE-K1-12
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Empty-place SchemeYes
Location of the lectureCampus Aalborg, Campus Esbjerg
Responsible for the module

Organisation

Study BoardStudy Board of Energy
FacultyFaculty of Engineering and Science
SchoolSchool of Engineering and Science