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Before choosing courseML1019 Applied Industrial Statistics 9.0 creditsAdministrate About course

The first 6 hp are fully identical with the “Fundamental Industrial Statistics, (ML1018) which is a prerequisite for understanding the later part of 3 hp which aims at practical application in more advanced areas.

The statistics is the foundation for applications important both for design, production, project planning with estimates of cost and time with uncertain values. Making predictions without taking into account the variation and uncertainty of values makes the result more unreliable. Using statistics and the extended method of Monte Carlo will increase the precision.

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus ML1019 (Spring 2013–)

Content and learning outcomes

Course contents

To make models for Monte Carlo-simulations it is necessary to know and understand the fundamental part constituting ML1018. Because of this it is practical that the study courses are read in parallel for the first 6 hp. Without the basic knowledge the possibilities to make useful models in Monte Carlo are too limited. Design of experiments contains parts which are not pure statistical but where a profound knowledge in the area is important to understand the variation and detect sources of error. Also in this study course a major part is to learn how to use computerized tools to improve the analysis.

Intended learning outcomes

After finished course the participant should:

  • Master the basic part according to ML1018
  • Be able to use orthogonal matrices in different areas like QFD, Conjoint Analysis and design of experiments
  • Be able to use multifactor standard arrays in experiment design
  • Be able to create models for analysis using Monte Carlo technique
  • Be able to make predictions with numerical analysis of risks and their probability

Course Disposition

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Literature and preparations

Specific prerequisites

For eligibility to the study course, the obligatory prior knowledge is a fundamental course in statistics like ML1018 or similar.

Recommended prerequisites

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Equipment

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Literature

  • Kompendium i Monte Carloanalys
  • Utdelat material för försöksplanering
  • Övningskompendium för dator-laborationer och Monte Carlo-övningar
  • Kursbok från ML1018

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • TEN1 - Written Exam, 4,0 hp, betygsskala: A, B, C, D, E, FX, F
  • ÖVN1 - Exercises, 2,0 hp, betygsskala: P, F
  • ÖVN2 - Exercises, 3,0 hp, betygsskala: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

  • Approved assignments, a final seminar with a seminar report, approved computer tasks ÖVN2 (3hp)
  • approved group assignments and a seminar report ÖVN1 (3 hp) ) (same for both ML1018 and ML1019) and a written examination TEN1 (3 hp) (same for both ML1018 and ML1019)

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Examiner

Profile picture Håkan Carlqvist

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web ML1019

Offered by

ITM/Sustainable Production Development

Main field of study

Technology

Education cycle

First cycle

Add-on studies

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