| Language of instruction: English |
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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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Linear Models DL (3577)
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5.0 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 | 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Knowledge of basic concepts from probability, statistics and distributions are required, and the student has knowledge of statistical inference.
The students also has knowledge of basic R programming and basic SAS.
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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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Self-study assignment ✔
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Semester 2 (6,00sp)
| Evaluation method | |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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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) | Copy slides, text books, notes, copies. |
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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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| Compulsory course material |
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R ans SAS are used as softwares in this course. |
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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 = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Knowledge of basic concepts from probability, statistics and distributions are required, and the student has knowledge of statistical inference.
The students also has knowledge of basic R programming and basic SAS.
|
|
|
|
|
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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Self-study assignment ✔
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Semester 2 (3,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 0 % |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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| Off campus online evaluation/exam | ✔ |
|
| For the full evaluation/exam | ✔ |
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|
|
| Use of study material during evaluation | ✔ |
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| Explanation (English) | Copy slides, text books, notes, copies. |
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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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| Compulsory course material |
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R ans SAS are used as softwares in this course. |
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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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