MS1411 Mathematical Statistics

Programme course, 7,5 Higher education credits, First cycle, spring semester 2022

This course is part of a programme and cannot be applied.

The purpose of the course is to obtain knowledge in probability theory as well as statistical theory and methods. Emphasis lies in probability theory and stochastic processes with technical applications.

Facts

  • Type of instruction: On campus, day, part-time 50%
  • Period : 2022-March-28 until 2022-June-05
  • Education level: G1F
  • Application: This course is part of a programme and cannot be applied.
  • Language of instruction: The language of instruction is English.
  • Location: Karlskrona
  • Main field of study: Mathematics
  • Course syllabus: Download
  • Welcome letter: This course is part of a programme and has no welcome letter.
  • Entry requirements: 15 ECTS in Mathematics accomplished.

Content

  • Combinatorics
  • Discrete and continuous stochastic variables in one dimension
  • Orientation about multivariate stochastic variables, independence
  • Various distributions, especially geometric, binomial, exponential, Poisson and normal (Gaussian) distributions as well as approximations
  • Expected value, variance, standard deviation, covariance, correlation
  • Markov chains
  • Markov processes in continuous time with applications in reliability theory
  • Point estimation including the ML-method
  • Interval estimation
  • Hypothesis testing
  • Simple linear regression
  • Applications in different technical fields

Learning outcomes

Knowledge and understanding
After completion of the course, the student should:
•master fundamental calculations with common one- and two-dimensional distributions, normal approximation included, as calculation of the mean, variance, standard deviation and hazard function.
• master the calculation of reliability of series and parallel circuits.
•know basic probability theory including basic theory for Markov processes.
•know statistical principles for point and interval estimation, tests of hypotheses and linear regression.
•know some of the most important applications of probability theory and statistical theory.

Skills and abilities
After completion of the course, the student should:
•be able to solve simple problems in reliability theory.
•be able to formulate and solve statistical problems in written form.
•know some of the most important terms of probability theory and statistical theory.

Judgement and approach
After completion of the course, the student should:
•be able to analyse, perform synthesis and to evaluate the results from a reasonability perspective.

Course literature and other teaching material

Walpole, R.E., Myers, R.H., Myers, S.L. & Ye, K. (2012 or later). Probability and Statistics for engineers and scientists. 9:th edition or later. Pearson. ISBN 0-321-74823-9.
Material from the Department of Mathematics and Natural Science.

Course literature and other teaching material

Walpole, R.E., Myers, R.H., Myers, S.L. & Ye, K. (2012 or later). Probability and Statistics for engineers and scientists. 9:th edition or later. Pearson. ISBN 0-321-74823-9.
Material from the Department of Mathematics and Natural Science.

Learning methods

Teaching is conducted through lectures and exercises. The course assumes that the student independently solves exercises throughout the course.

Work placement

No work placement is included in the planned learning activities. BTH is aiming for a close contact with the surrounding community when developing courses and programmes.

Teachers

Time allocation

On average, a student should study 200 hours to reach the learning outcomes.
This time includes all the various available learning activities (lectures, self studies, examinations, etc.).
This estimation is based on the fact that one academic year counts as 60 ECTS credits,
corresponding to an average student workload of 1 600 hours. This may vary individually.

Assessments

Component examinations for the course
Code Title ECTS credits Grade
1705 Examination 7.5 A-F

Grading

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Insufficient, supplementation required, F Fail.

Exams

More information about exams are found in the Student's Portal, where you also enrolls for most exams.


There might be other scheduled examinations. Information regarding these examinations are available in the learning platform Canvas or at other places that the person who is responsible of the course will refer to.

Course Evaluation

The course manager is responsible for the views of students on the course being systematically and regularly gathered and that the results of the evaluations in various forms affect the form and development of the course.

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