Introduction to Mathematical and Statistical Modelling
Cod: 21167
Department: DCET
Scientific area: Mathematics
Total working hours: 156
Total contact time: 26

The goal of this learning unit is to introduce the students to themes and techniques of Mathematical and Statistical Modelling. Modelling is an area of great importance for the applications and although a deeper understanding of how to model real life problems in Science and Technology requires an advanced postgraduate training, it is important that a graduate from an applied mathematics course to have acquired some basic knowledge in his undergraduate training. In this learning unit we start by introducing basic modelling concepts and tools, such as dimensional analysis, and then each student will explore two modelling topics, to be chosen from a set of at least four that will vary from year to year. The student’s choice is free, although it is intended the student’s will choose a set of modules that will provide the acquisition of knowledge and competencies as coherent as possible.

Mathematical Modelling
Statistical Modelling

To know de basic foundations of Mathematical and statistical modelling and know how to apply them to concrete situations of suitable difficulty level.

  1. General modelling principles.
  2. Dimensional analysis.
  3. Module 1: Linear regression in statistical modelling.
  4. Module 2: Introduction to numerical integration.
  5. Module 3: Modeling through Boolean and Fuzzy Logics
  6. Module 4: Elements of risk analysis and applications.

For the first two introductory points of the syllabus the following book will be used:

  • Holmes, M. H., Introduction to the Foundations of Applied Mathematics, Texts in Applied Mathematics, vol. 56, Springer, New York, 2009.

Further bibliography will be given in each module.


Continuous assessment is privileged: 2 digital written documents (e-folios) during the semester (40%) and a final digital test, Global e-folio (e-folio G) at the end of the semester (60%). 

Being this learning unit in the last year of the undergraduate programme, i tis assumed that the student has acquired a working knowledge in Mathematical Analysis, Linear Algebra, Numerical Analysis, Probability, and Statistics that allow him to recall and use them whenever needed.