| 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 | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical |  |
| 1st year Master Bioinformatics - icp | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 1st year Master Biostatistics | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 1st year Master Biostatistics - icp | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 1st year Quantitative Epidemiology | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 1st year Master Quantitative Epidemiology - icp | 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 design methodology. | | | - 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 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 |
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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 t ables (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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Lecture ✔
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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 | Only the score on the project of part II of the course is 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 | |
|
| 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) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
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| 1st year Master Data Science | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| | | 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 t ables (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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Lecture ✔
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Self-study assignment ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
|
| 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 | |
|
| Explanation (English) | The score on the project of the second period is carried over to the retake exam (weight 15%). |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
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| 1st Master of Business and Information Systems Engineering | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
| 2nd Master of Business and Information Systems Engineering | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The holder of the degree communicates clearly and correctly in writing and orally, in a business and academic context, if necessary supplemented with visual support. (Communication) | - EC
| The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| The holder of the degree shows autonomy in analysing, interpreting, evaluating and reporting research results. (Research skills) |
|
| | 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
Part I
- Standard descriptive and inferential methods for multiway contingency t ables (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).
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 15 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Written exam | 85 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies, project report |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The group project of part II is compulsory |
|
|
|
| Consequences | Missing the group project results in a 0 final score. |
|
|
|
| Additional information | Only the score on the project of part II of the course is carried over to the retake exam.
Possible use of group evaluation of the project of part 2, with individual adjustment of the group score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The score on the project of the second period is carried over to the retake exam (weight 15%). |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
|
|
|
| Exchange Programme Mathematics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
|
| |
|
|
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 t ables (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).
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 15 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Written exam | 85 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies, project report |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The group project of part II is compulsory |
|
|
|
| Consequences | Missing the group project results in a 0 final score. |
|
|
|
| Additional information | Only the score on the project of part II of the course is carried over to the retake exam.
Possible use of group evaluation of the project of part 2, with individual adjustment of the group score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The score on the project of the second period is carried over to the retake exam (weight 15%). |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
|
|
|
| Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| WET 2. The newly graduated student can independently set up and conduct research relevant to his field, consisting of a critical literature study, formulating a research question and hypothesis, selecting and optimizing suitable methods and techniques, critically analyzing and interpreting the results, formulating conclusions and reporting of the findings. |
|
| | 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
Part I
- Standard descriptive and inferential methods for multiway contingency t ables (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).
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 15 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Written exam | 85 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies, project report |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The group project of part II is compulsory |
|
|
|
| Consequences | Missing the group project results in a 0 final score. |
|
|
|
| Additional information | Only the score on the project of part II of the course is carried over to the retake exam.
Possible use of group evaluation of the project of part 2, with individual adjustment of the group score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The score on the project of the second period is carried over to the retake exam (weight 15%). |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
|
|
|
| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| |
|
|
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 t ables (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).
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 15 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Written exam | 85 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies, project report |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The group project of part II is compulsory |
|
|
|
| Consequences | Missing the group project results in a 0 final score. |
|
|
|
| Additional information | Only the score on the project of part II of the course is carried over to the retake exam.
Possible use of group evaluation of the project of part 2, with individual adjustment of the group score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
|
|
|
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 |
|