Course syllabus

Jump to today

Course-PM

MMS075, Statistical modeling in logistics, lp3 VT21 (7.5 hp)

The course is offered by the Department of Mechanics and Maritime Sciences at Chalmers University of Technology.

Examiner

András Bálint

Course purpose

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.

Schedule

TimeEdit, see a direct link here.

Course literature

Most of the course content is based on the following book, which is freely available online:

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning: With Applications in R. New York: Springer Science+Business Media, LLC

The content of this book (to be abbreviated by ISL below) is covered in the online course Statistical Learning that is available for free and in self-paced mode. The course lecture videos are also available here.

The course includes computer labs using the programming language and statistical environment R. Most of the content is based on the codes provided in the ISL book. Further resources for learning R are available at the R Tutorial and RStudio support web pages.

Course design

The course includes:

  • Lectures;
  • Exercise classes;
  • Computer labs;
  • Consultation times.

Both lectures and exercise classes will include the presentation of new study material and exercise solving sessions. Computer labs address the practical implementation of the statistical methods using R software codes. It is not compulsory but strongly recommended to attend lectures, exercise classes, and computer labs, because it is expected to be difficult to understand the course content and do the project assignments otherwise.

Note: due to the Covid-19 situation, all classes in MMS075 during the academic year 2020/21 are held online. There are rooms available on campus for the lectures, exercises, and computer labs (see TimeEdit and the schedule), but the teacher is only available online.  

Consultation times are available each week during the course. During these times, the examiner is available for discussing course material. Consultation may occur by appointment.

Appointments with the examiner and other communication take place on Canvas or by e-mail.

Learning objectives

After 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.

Examination form

Compulsory elements include passing three individual project assignments and a written online exam. The final grade is determined by the grade on the written exam.

You will have at least one week to work on each project assignment. The deadlines are strict. Delays must be indicated before the deadlines and motivated by good, provable reasons. 

Deadlines for the assignments:

  • Assignment 1: Friday, Feb 12, 2021;
  • Assignment 2: Monday, Feb 22, 2021;
  • Assignment 3: Monday, Mar 8, 2021.

Exam dates:

  • Ordinary exam: Monday, Mar 15, 2021, am;
  • Re-exam 1: Thursday, Jun 10, 2021, pm;
  • Re-exam 2: Wednesday, Aug 18, 2021, pm.

Course summary:

Date Details Due