| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Bioinformatics - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical |  |
| 1st year Master Biostatistics - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 1st year Master Quantitative Epidemiology - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | 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 design 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 knows the international nature of the field of statistical science and data science. | - 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including
Part I
- Standard descriptive and inferential methods for multiway contingency tables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures)
- Components of a generalized linear model (GLM)
- GLM for binary data: logistic regression
- Building and applying logistic regression models
- Overdispersion and quasi-likelihood
- Conditional logistic regression and exact distributions
Part II
- Extensions to multinomial responses (baseline category, cumulative link, partial odds ratio,)
- Extensions to clustered binary (GEE, random effects)
- Extensions to clustered & multinomial data
- Loglinear models
- Models for matched pairs.
The student should be able to apply such models and methods using appropriate software (SAS, R).
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Distance learning ✔
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Project ✔
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Self-study assignment ✔
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Period 2 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 15 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Written exam | 85 % |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Copy slides, text books, notes, copies, project report. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The group project of part II is compulsory. |
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| Consequences | Missing the group project results in a 0 final score. |
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| Additional information | The scores of the homeworks and the project of part II are carried over to the retake exam.
Possible use of group evaluation of the project of part 2, with individual adjustment of the group score. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The score on the project of the second period is carried over to the retake exam (weight 15%). |
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| Compulsory textbooks (bookshop) |
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Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
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| 1st year Master Data Science - distance learning | Compulsory | 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 design 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 knows the international nature of the field of statistical science and data science. | - 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. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including
- Standard descriptive and inferential methods for multiway contingency tables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures)
- Components of a generalized linear model (GLM)
- GLM for binary data: logistic regression
- Building and applying logistic regression models
- Overdispersion and quasi-likelihood
- Conditional logistic regression and exact distributions
The student should be able to apply such models and methods using appropriate software (SAS, R).
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Distance learning ✔
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Project ✔
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Self-study assignment ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 0 % |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Copy slides, text books, notes, copies, project report. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory textbooks (bookshop) |
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Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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