Statistics, First Course, 4 credits

Statistisk teori, grk, 4 hp

TAMS24

Main field of study

Mathematics Applied Mathematics

Course level

First cycle

Course type

Programme course

Examiner

Zhengxia Liu

Director of studies or equivalent

Ingegerd Skoglund

Education components

Preliminary scheduled hours: 44 h
Recommended self-study hours: 63 h

Available for exchange students

Yes
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CMED Biomedical Engineering, M Sc in Engineering 5 (Autumn 2017) 1 4 Swedish/English Linköping C
6CDDD Computer Science and Engineering, M Sc in Engineering 7 (Autumn 2017) 1 4 Swedish/English Linköping E

Main field of study

Mathematics, Applied Mathematics

Course level

First cycle

Advancement level

G2X

Course offered for

  • Biomedical Engineering, M Sc in Engineering
  • Applied Physics and Electrical Engineering - International, M Sc in Engineering
  • Applied Physics and Electrical Engineering, M Sc in Engineering
  • Computer Science 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, matrix algebra and a first course in probability theory.

Intended learning outcomes

The course is intended to give basic knowledge of the theory and methods of statistical inference, i.e. how to use observed data to draw conclusions about phenomena influenced by random factors. By the end of the course, the student should be able to:

  • use an appropriate probability model to describe and analyse observed data and draw conclusions concerning interesting parameters;
  • derive point estimators of parameters and analyse their properties;
  • understand the principles of statistical inference based on confidence intervals and hypothesis testing;
  • construct confidence intervals and test hypotheses using observed data, draw conclusions and describe the uncertainty;
  • explore the nature of the relationships between two or several variables by using simple or multiple linear regression models and discuss the adequacy of the models;
  • find probability models and statistical methods in applications from engineering, economy and science and evaluate the results;
  • use appropriate statistical software for certain types of statistical analyses.

Course content

Chi-square-, t-, F-distribution. Point estimation, properties of estimators, the method of maximum likelihood, the method of moments and the least squares method. Confidence intervals and tests of hypotheses for one or several samples especially for normal, binomial and Poisson distribution and certain cases when the central limit theorem can be applied. Chisquare tests. Random vectors, mean vectors and covariance matrices. The multivariate normal distribution.
Multiple regression, estimation of parameters, confidence intervals, prediction, analysis of variance table, selection of regression model.
Statistical software is used for regression analysis.

Teaching and working methods

The teaching consists of lectures and lessons. Obligatory computer exercises are also included in the course.

Examination

TEN1Written examination3 creditsU, 3, 4, 5
LAB1Laboratory work1 creditsU, G

Grades

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

Other information

Supplementary courses: Statistical Inference - advanced Course, Experimental Design, Multivariate statistical analysis. 

Department

Matematiska institutionen

Director of Studies or equivalent

Ingegerd Skoglund

Examiner

Zhengxia Liu

Course website and other links

http://courses.mai.liu.se/GU/TAMS24

Education components

Preliminary scheduled hours: 44 h
Recommended self-study hours: 63 h

Course literature

Additional literature

Books

  • G.Blom, J Enger, G. Englund, J. Grandell, L. Holst, Sannolikhetsteori och statistikteori med tillämpningar Studentlitteratur

Compendia

  • E. Enqvist, Grundläggande regressionsanalys

Other

Code Name Scope Grading scale
TEN1 Written examination 3 credits U, 3, 4, 5
LAB1 Laboratory work 1 credits U, G

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

Additional literature

Books

G.Blom, J Enger, G. Englund, J. Grandell, L. Holst, Sannolikhetsteori och statistikteori med tillämpningar Studentlitteratur

Compendia

E. Enqvist, Grundläggande regressionsanalys

Other

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)
X
X
X
TEN1

                            
1.2 Fundamental engineering knowledge (G1X level)

                            
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)

                            
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)

                            
1.5 Insight into current research and development work

                            
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving
X
X
X
LAB1
TEN1

                            
2.2 Experimentation, investigation, and knowledge discovery
X
X
LAB1

                            
2.3 System thinking

                            
2.4 Attitudes, thought, and learning
X
TEN1

                            
2.5 Ethics, equity, and other responsibilities
X
LAB1
TEN1

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork
X
LAB1

                            
3.2 Communications
X
LAB1

                            
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context

                            
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management

                            
4.4 Designing

                            
4.5 Implementing

                            
4.6 Operating

                            
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects
X
X
TEN1

                            

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