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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MMS075 - Statistical modeling in logistics
Statistisk modellering med logistiktillämpningar
 
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: TSLOG
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: First-cycle
Major subject: Shipping and Marine Technology
Department: 30 - MECHANICS AND MARITIME SCIENCES


Teaching language: English
Application code: 77122
Open for exchange students: Yes
Maximum participants: 30
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0119 Written and oral assignments, part A 5,0c Grading: UG   5,0c    
0219 Examination, part B 2,5c Grading: TH   2,5c   16 Mar 2020 am L,  10 Jun 2020 pm L,  19 Aug 2020 am L

In programs

TSLOG SHIPPING AND LOGISTICS, Year 3 (compulsory elective)

Examiner:

András Bálint

  Go to Course Homepage


Eligibility:

In order to be eligible for a first cycle course the applicant needs to fulfil the general and specific entry requirements of the programme(s) that has the course included in the study programme.

Course specific prerequisites

Knowledge and skills equivalent to the learning outcomes of the following courses:

SJO915 Applied statistics

Aim

The course aims to give the students skills in statistical modeling on larger data sets linked to the logistics area. The students get to develop their skills in applying the theoretical knowledge they have acquired in previous courses on large, unstructured data sets.

Learning outcomes (after completion of the course the student should be able to)

- Demonstrate an understanding of the key concepts and ideas in statistical modeling on larger datasets
- Describe suitable statistical methods for using on larger datasets relevant in logistics
- Choose and use appropriate statistical methods for answering a logistics related problem, and report the findings in a suitable and compelling format
- Critically evaluate statistical materials and methods and reason about their limitations
- Reflect on ethical aspects and considerations when collecting and analyzing larger datasets

Content

- Key concepts in statistical modeling with a focus on larger datasets
- Statistical methods relevant for statistical modeling in logistics
- Opportunities and limitations of different statistical methods
- Reporting statistical findings in a compelling way
- Ethical aspects on collecting and analyzing data

Organisation

The course consists of one or more project assignments together with lectures and a written exam.

Literature

See course homepage.

Examination including compulsory elements

One or more project tasks (part A). Written examination (part B). The final grade is determined by the grade on the written exam.


Published: Wed 26 Feb 2020.