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ML1018 Fundamental Industrial Statistics 6.0 credits

Administer About course

In all production today the need of understanding statistics and stochastic processes is very large. To control design and production without availability and understanding of statistics is virtually impossible. To adjust a machine and the operator has no understanding of variance and variation will make the process oscillate and the quality will suffer.

The needed statistics are of course based on mathematical statistics but is in its appearance quite different. This makes it difficult to bridge the gap between theory and application and thus use the pure theoretical statistics in every day applications.

The study course is therefore aimed at creating understanding for the practical use of statistics and the possible advantages the engineer will have from this in the professional life.

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 ML1018 (Autumn 2024–)
Headings with content from the Course syllabus ML1018 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Probability theory

•    Distributions and fundamental definitions and theorems.
•    Reliability.

Statistics

•    Descriptive statistics.
•    Methodology for quality and availability.
•    Point estimations.
•    Assessments from insufficient (censored) data.
•    Interval estimations and statistical tests.
•    Regression analysis.
•    Design of experiments.

Intended learning outcomes

After completed course, the student should be able to:

•    solve problems within probability theory
•    solve problem within statistics
•    estimate probabilities with simulation
•    apply some methodology for process improvement, for example
•    use concepts within descriptive statistics and illustrate data in different diagrams using software
•    implement a simple analysis of a time series
•    implement fundamental design of experiments, for example factor analysis

Literature and preparations

Specific prerequisites

Completed course SF1625 or then equivalent.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

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

Examination

  • ÖVN1 - Exercises, 2.0 credits, grading scale: P, F
  • TEN1 - Written Exam, 4.0 credits, grading scale: A, B, C, D, E, FX, 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.

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

Examiner

Profile picture Oswald Fogelklou

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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

ITM/Production Engineering Södertälje

Main field of study

Technology

Education cycle

First cycle