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.
ML1019 Applied Industrial Statistics 9.0 credits
This course has been discontinued.
Last planned examination: Spring 2016
Decision to discontinue this course:
No information insertedThe 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.
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus ML1019 (Spring 2013–)Content and learning outcomes
Course contents
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
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
Equipment
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
Examination
- TEN1 - Written Exam, 4.0 credits, grading scale: A, B, C, D, E, FX, F
- ÖVN1 - Exercises, 2.0 credits, grading scale: P, F
- ÖVN2 - Exercises, 3.0 credits, grading scale: 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
Opportunity to raise an approved grade via renewed examination
Examiner
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.