Course: Statistics 2 credits: 5
- Course code
- CTVH24STATIS2
- Name
- Statistics 2
- Study year
- 2025-2026
- ECTS credits
- 5
- Language
- Dutch, with parts in English
- Coordinator
- M.E.F. Apol
- Modes of delivery
-
- Practical / Training
- Tutorial
- Assessments
-
- Statistiek 2 Practicum - Other assessment
- Statistiek 2 Theorie - Computer, organised by STAD examinations
Learning outcomes
This module has the following learning outcomes:
You perform linear and non-linear regression (ordinary and multiple) for a given or experimentally obtained dataset, interpret the data and apply model selection.
You use simultaneous Design of Experiment (DoE) methods to optimise factors for a given process, choosing the right design and using regression and model selection (Surface Response Method) to locate the optimum.
You use univariate and multivariate sequential optimisation methods (Binary, Fibonacci, Simplex and modified Simplex) for optimisation of factors for a given process, and use them to localise the optimum.
You select the appropriate techniques and methods in a screening phase and in a detailed optimisation phase.
You use matrix algebra as part of optimisation of process parameters and can use it to determine the quality of an experimental design.
You perform linear and non-linear regression (ordinary and multiple) for a given or experimentally obtained dataset, interpret the data and apply model selection.
You use simultaneous Design of Experiment (DoE) methods to optimise factors for a given process, choosing the right design and using regression and model selection (Surface Response Method) to locate the optimum.
You use univariate and multivariate sequential optimisation methods (Binary, Fibonacci, Simplex and modified Simplex) for optimisation of factors for a given process, and use them to localise the optimum.
You select the appropriate techniques and methods in a screening phase and in a detailed optimisation phase.
You use matrix algebra as part of optimisation of process parameters and can use it to determine the quality of an experimental design.
Content
As a chemical technologist, you will often be involved in improving and optimising a production process. To give well-founded advice, you will often do a number of experiments. This module covers various optimisation techniques, with the aim of obtaining as much relevant information as possible with as few experiments as possible. Simultaneous (Design of Experiment, Surface Response Method) and sequential methods (Binary, Fibonacci, Simplex and Modified Simplex) approaches are discussed and applied to practical problems. Assessment is by means of a computer-based examination using Excel, Mathcad and Wolframalpha.
Included in programme(s)
School(s)
- Institute for Life Science & Technology