Syllabus academic year 2010/2011
(Created 2010-07-25.)
Credits: 7,5. Grading scale: TH. Cycle: G2 (First Cycle). Main field: Technology. Language of instruction: The course will be given in Swedish. FMS035 overlaps following cours/es: FMS012, FMS030, FMS032, FMS033, FMS086, FMS140, FMS601 and FMSF01. Compulsory for: M3. Course coordinator: Anna Lindgren, Director of studies,, Mathematical Statistics. Prerequisites: At least 6 university credits within the courses FMAA01/FMAA04, and FMA430/FMA435/FMA025. Recommended prerequisits: Calculus in one and several variables and Linear algebra. Assessment: Written exam and computer exercises. The course grade is based on the exam grade. Parts: 2. Further information: The course may not be included together with FMS601 or FMSF01. Home page:

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 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 to their colleagues. They will also be expected to be able to read and assess the analyses of others.

The course shall also give a basis for further studies, both in probability theory and inference theory, as well as in the application areas, such as design of experiments, automatic control, process control, and logistics.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

Judgement and approach
For a passing grade the student must

Expectation and variance. Normal distribution, binomial distribution, and other important distributions for measurements and frequencies. Data analysis. Statistical inference: Point estimates, interval estimates, and hypothesis testing. Methods for normally distributed observations. Approximative methods based on the normal distribution. Comparisons between expectations, variability, and distributions. Estimates of proportions. Regression analysis and calibration. Concepts and methods in quality control. Examples are chosen with respect to mechanical engineering.

Blom, G, Enger, J, Englund, G, Grandell, J, Holst, L: Sannolikhetsteori och statistikteori med tillämpningar. Studentlitteratur 2005. ISBN:91-44-02442-8


Code: 0108. Name: Examination.
Higher education credits: 7. Grading scale: TH. Assessment: Written exam.

Code: 0208. Name: Laboratory Work.
Higher education credits: 0,5. Grading scale: UG. Assessment: Computer exercises.