Course syllabus - Statistical Analysis in Industrial Systems

Objectives

Modern industrial plants and environments measure and store all relevant production variables. In addition to observation, the data can be obtained also by experimentation. The course provides fundamental elements of applied statistical analysis that can be used to analyze and model the data obtained from industrial plants. The elements of probability theory that support statistical concepts are also introduced, as there is a need for a deeper mathematical understanding. An introduction to most useful data storage and manipulation techniques is also given.

Learning outcomes

After the course, the students are expected to: 
1. Have an understanding of the common organization of industrial information systems 
2. Have an overview and understanding of relation database management systems and be able to do SQL queries to extract the data  
3. Have an overview of available statistical tools and be able to do relevant data manipulation and visualization for the purpose of explorative data analysis 
4. Have a mathematical understanding of the most fundamental concepts of statistics and probability theory. 
5. Have a working knowledge of analysis of variance (ANOVA), regression modeling, and applying statistical test to the data 
6. Be able to correctly interpret the results of the statistical analysis

Course content

* Distributed control system and functional levels of industrial control systems 
* Level 2 control system software architecture 
* Data storage techniques and overview of relational databases 
* SQL query language and data import to a statistical tool 
* Overview of available statistical tools 
* Introduction to R software environment, data preparation, and data visualization 
* Elements of probability theory and mathematical statistics and their application on data from industrial plants 
* Regression and analysis of variance (ANOVA) models for industrial data. 
* Fitting regression models and applying statistical tests (students can analyze their own data, or take example data sets from the literature)

Specific requirements

90 credits of which at least 60 credits within natural science or engineering, including at least 7.5 credits in computer programming, and 7.5 credits in Single Variable Calculus. The mathematics shall include knowledge of elementary calculus: integrals, derivations, series, and sums. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

Examination

Written examination at home (HEM1), 0.5 credits, examines the learning objectives 1-3, marks Fail (U), 3, 4 or 5.
Written examination at home (HEM2) 1 credit, examines the learning objectives 4 and 5, marks Fail (U), 3, 4 or 5. 
Project (PRO1), 1 credit, an assignment that is presented with a report of the project, examines learning outcomes 2, 3, 5, and 6, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass with credit, Pass, Fail