Course syllabus for Applied statistical analysis and experimental design

Course syllabus adopted 2026-02-20 by Head of Programme (or corresponding).

Overview

  • Swedish nameTillämpad statistik och försöksplanering
  • CodeMVE790
  • Credits7.5 Credits
  • OwnerTKTDE
  • Education cycleFirst-cycle
  • Main field of studyIndustrial Design Engineering
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language Swedish
  • Application code 70120
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0126 Written and oral assignments 1.5 c
Grading: UG		1.5 c
0226 Examination 6 c
Grading: TH		6 c

In programmes

Examiner

Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Specific entry requirements

The same as for the programme that owns the course
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Course specific prerequisites

Basic knowledge of mathematical analysis (calculus) and linear algebra.

Aim

This course covers the fundamentals of probability theory and statistics, with a particular focus on topics of relevance to studies in engineering design. Emphasis is placed on basic statistical data analysis and experimental design.

Learning outcomes (after completion of the course the student should be able to)

  • Describe fundamental concepts in probability theory and how they are used in statistical theory and modelling.
  • Interpret basic descriptive statistical representations.
  • Explain the principles of point and interval estimation as well as hypothesis testing, including the interpretation of p-values and confidence intervals under specific assumptions.
  • Describe the fundamentals of linear regression and experimental design, including underlying assumptions and limitations.
  • Perform basic probability calculations.
  • Carry out descriptive data analysis and data visualisation using statistical software.
  • Describe different kinds of parameter estimators and their associated statistical properties and apply them to data sets using statistical software.
  • Test hypotheses for populations.
  • Model and analyse data by means of regression analysis.
  • Perform basic experimental design.
  • Critically evaluate assumptions, model choices and conclusions.

Content

Probability Theory:
  • Basic concepts in probability theory, such as outcomes, events, probability, (in)dependent events, conditional probability, basic combinatorics, the law of total probability and Bayes’ theorem.
  • Discrete and continuous one- and multi-dimensional random variables, with emphasis on distributions commonly used in statistical methodology, e.g. normal and binomial distributions.
  • Distributional descriptive statistics/summary measures such as (conditional) expectation, variance and quantiles.
  • Limit theorems and their applications in approximating distributional properties.
Statistics:
  • Data collection and data types.
  • Graphical representation of data.
  • Empirical descriptive statistics.
  • Point estimation of distribution parameters and their properties - theory and applications.
  • Confidence intervals for estimation of population parameters for one and two populations - theory and applications.
  • Hypothesis testing for population parameters for one and two populations, including p-values - theory and applications.
  • Linear regression - theory and applications.
  • Experimental design - theory and applications.

Organisation

Lectures, exercise sessions/computer labs and project work.

Literature

"Probability & Statistics with R for Engineers and Scientists" (2016) by M. Akritas

Examination including compulsory elements

Written examination and project.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.

Applied statistical analysis and experimental design | Chalmers