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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Generalized Linear Models DL (3580)
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6.0 stptn |
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Change the language, year or choose another item in the dropdown list if it is available.
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Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| second year Master Biostatistics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Quantitative Epidemiology - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student should be familiar with statistical inference and statistical models.
The student should be familiar with programming in R.
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Introduction to the diffferent steps (and their integration) of microbial risk assessment
Statistical, mathematical and simulation methodology for
- models for quantitative risk assessment
- models for exposure assessment (concentration distribution, limit of detection,distribution of number of organisms)
- models for the quantification of consumption data and dealing with correlated inputs
- mechanistic and empirical dose response models, infection versus illness, model averaging.
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Semester 1 (3,00sp)
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| Written evaluation during teaching period | 50 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Slides, course notes |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | Score for the project is carried over to the retake exam. |
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| Compulsory course material |
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Lecture notes will be made available via blackboard.
The R software will be used in this course. |
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| Recommended reading |
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[Quantitative Microbial Risk Assessment],[Charles N. Haas; Joan B. Rose; Charles P. Gerba],[],[Wiley],[9780471183976],[] |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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