Course syllabus

# Matematisk statistik Mathematical Statistics

## FMSF70, 7,5 credits, G2 (First Cycle)

Valid for: 2017/18
Decided by: PLED I
Date of Decision: 2017-04-10

## General Information

Main field: Technology.
Compulsory for: B3, BME3, K3, N3
Language of instruction: The course will be given in Swedish

## Aim

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 emphasis lies on models and methods for analysis of experimental data and measurement errors.

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 to 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, particularly in design of experiments and methods for multidimensional data (Chemometrics).

## Learning outcomes

Knowledge and understanding
For a passing grade the student must

• be able to relate questions regarding random variation and observed data to the concepts of random variables, distributions, and relationships between variables,
• be able to explain the concepts of independence, probability, distribution, expectation, and variance,
• be able to calculate the probability of an event and the expectation from a given distribution,
• be able to describe fundamental techniques for statistical inference and be able to use them on basic statistical models,
• be able to explain the purpose and principles of experimental design.

Competences and skills
For a passing grade the student must

• be able to construct a simple statistical model describing a problem based on a real life situation or on a collected data material,
• be able to use a computational program for simulation and interpretation of statistical models, as well as for data analysis,
• be able to choose, perform, and interpret a statistical procedure that answers a given statistical problem,
• be able to use statistical terms within the field in writing,
• be able to present a statistical analysis in a technical report.

Judgement and approach
For a passing grade the student must

• be able to examine a statistical model and its ability to describe reality.

## Contents

The basis in probability theory and inference, confidence intervals, statistical methods such as design of experiments and regression analysis. Applications: measurement value analysis, different types of errors and their propagation; comparisons of means and variations; concepts and methods for quality control, estimations of proportions; regression analysis, calibration; factorial designs, optimization of experimental parameters, response surfaces. Applications in chemical and biotechnical engineering are of particular interest.

## Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam, computer exercises, project report 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: 0117. Name: Computational Ability Test.
Credits: 0,5. Grading scale: UG. Assessment: Computer based test
Code: 0217. Name: Examination.
Credits: 5,5. Grading scale: TH. Assessment: Written exam
Code: 0317. Name: Project Work.
Credits: 1. Grading scale: UG. Assessment: Written project report.
Code: 0417. Name: Laboratory Work.
Credits: 0,5. Grading scale: UG. Assessment: Computer exercises

Required prior knowledge: Calculus in one variable.
The number of participants is limited to: No
The course overlaps following course/s: FMS012, FMSF01, KKK065, MAS217, MASB02, FMS032, FMS033, FMS035, FMS085, FMS140, FMS601, FMSF20, FMS086, FMSF45