Statistics, 6 credits (TNG006)

Matematisk statistik, 6 hp

Main field of study

Mathematics Applied Mathematics

First cycle

Programme course

George Baravdish

Director of studies or equivalent

George Baravdish
Course offered for Semester Period Timetable module Language Campus VOF
6CIEN Electronics Design Engineering, M Sc in Engineering 4 (Spring 2017) 2 1 Swedish Norrköping o
6CKTS Communication and Transportation Engineering, M Sc in Engineering 4 (Spring 2017) 2 1 Swedish Norrköping o
6CMEN Media Technology and Engineering, M Sc in Engineering 4 (Spring 2017) 2 1 Swedish Norrköping o

Main field of study

Mathematics, Applied Mathematics

First cycle

G2X

Course offered for

• Electronics Design Engineering, M Sc in Engineering
• Communication and Transportation Engineering, M Sc in Engineering
• Media Technology and Engineering, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Calculus in several variables

Intended learning outcomes

The course is intended to teach students to understand and use basic probability and statistical theory, that is, the theory for dealing with random experiments. The emphasis is on developing the statistical background required for use in engineering, economy and natural sciences. After the course the students should be able to:

• Model and use both events and stochastic variables in different problems
• Use conditional probability
• Use the multiplication rule and combinations
• Good understanding, derivation and use of distribution functions
• Decide whether given random variables are independent
• Calculate the expected value and variance for functions of stochastic variables
• Use the Central Limit Theorem
• Point estimation by Maximum likelihood, moment and least squares methods
• Determine interval of confidence for mean and variance
• Testing a hypothesis
• Use regression analysis

Course content

• Probability theory: Sample space, events and probabilities. Combinatorics. Conditional probabilities and independent events. Discrete and continuous random variables, their probability distributions, expectations and variances. Normal, exponential, binomial, Poisson distributions etc. Functions of random variables. The central limit theorem.
• Statistics: Point estimation. Properties of estimators. The method of maximum likelihood, the method of moments and the least squares estimation. Confidence intervals. Testing statistical hypotheses. Simple linear regression.

Teaching and working methods

Lectures and sessions of exercises.

Examination

 KTR1 Written test U, G 0 credits TEN1 Written examination U, 3, 4, 5 6 credits

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för teknik och naturvetenskap

George Baravdish

Examiner

George Baravdish

http://www2.itn.liu.se/utbildning/kurs/

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h

Course literature

Gunnar Blom m. fl.: Sannolikhetsteori och statistikteori med tillämpningar. (Studentlitteratur) Problemsamling för kursen TNG006. Formelsamling i matematisk statistik (utgiven av ITN)
Gunnar Blom m. fl.: Sannolikhetsteori och statistikteori med tillämpningar. (Studentlitteratur) Problemsamling för kursen TNG006. Formelsamling i matematisk statistik (utgiven av ITN)