Valid for: 2020/21
Decided by: PLED I
Date of Decision: 2020-04-03
Main field: Technology.
Compulsory for: D3, E3
Language of instruction: The course will be given in Swedish
The course is intended to give the student the basics in mathematical modelling of random variation and an understanding of the principles behind statistical analysis. It shall also give the students a toolbox containing the most commonly used models and methods, as well as the ability to use these in practical situations. The student shall also be able to handle dependence between observations.
The course fills two purposes, providing a fundamental knowledge of mathematical statistics, as well as giving a foundation for further studies.
The fundamental knowledge is essential for those who, in their professional lives, will not necessarily be involved in statistical analyses on a daily basis, but who, on occasion, will be expected to perform basic statistical tests and present the results for their colleagues. They will also be expected to be able to read and assess the analyses of others.
The main purpose of the course is to provide a basis for further studies. Both in stochastic modelling and analysis of random phenomena in time and/or space, and in the application areas, such as telecommunications, economics, signal processing, logistics, and process control. The focus therefore lies in probability theory and stochastic modelling of both independent and dependent data.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
Data analysis. Descriptive statistics. Axioms of probability. Conditional probability, independent events. Stochastic variables and functions of the same. Expectation, variance, and covariance. Normal distribution, binomial distribution, and other important distributions for measurements and frequencies. Conditional distributions and conditional expectations. Point estimates and their properties. Maximum likelihood and Least squares. Principles of interval estimates and hypothesis testing. Methods for normally distributed observations. Approximative methods based on the normal distribution. Estimates of proportions. Correlation. Linear univariate and multiple regression.
Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam, computer exercises and computational ability test
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Parts
Code: 0115. Name: Examination.
Credits: 6. Grading scale: TH. Assessment: Written examination.
Code: 0215. Name: Laboratory Work.
Credits: 1. Grading scale: UG. Assessment: Computer exercises.
Code: 0315. Name: Computational Ability Test.
Credits: 0,5. Grading scale: UG. Assessment: Computer based test
Assumed prior knowledge: Calculus in one and several variables and Linear algebra.
The number of participants is limited to: No
The course overlaps following course/s: FMSF30, FMSF35, FMSF40, FMSF45, MASB03, FMSF50, FMSF55, FMSF70, MASB02, FMSF75, MASA01
Director of studies: Johan Lindström, studierektor@matstat.lu.se
Course homepage: http://www.maths.lth.se/matstat/kurser/fmsf20/