| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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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 (3560)
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5.0 stptn |
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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. |
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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 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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Self-study assignment ✔
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Period 2 Credits 6,00
| 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 | ✔ |
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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 and SAS will be used as softwares in this course. |
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| 1st year Master Data Science | 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 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 |
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|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
| Compulsory course material |
| |
R and SAS will be used as softwares in this course. |
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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) |
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| | 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
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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|
|
|
Self-study assignment ✔
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|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written exam | 100 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
| Compulsory course material |
| |
R and SAS will be used as softwares in this course. |
|
|
|
|
|
| Exchange Programme Mathematics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
|
| |
|
|
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
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 ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written exam | 100 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
| Compulsory course material |
| |
R and SAS will be used as softwares in this course. |
|
|
|
|
|
| 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 | |
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| | | Learning outcomes |
- EC
| 5.5. The master of education is a domain expert SCIENCES: the EM can independently set up and conduct research relevant to his field of study consisting of a critical literature review, formulating a research question and hypothesis, selecting and optimising suitable methods and techniques, critically analysing and interpreting the results, formulating conclusions and reporting the findings. |
|
| | 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
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 ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Written exam | 100 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
| Compulsory course material |
| |
R and SAS will be used as softwares in this course. |
|
|
|
|
|
| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| |
|
|
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 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 exam | 100 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | Copy slides, text books, notes, copies |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory textbooks (bookshop) |
| |
Categorical Data Analysis,Agresti, Alan,3rd edition,Wiley,9780470463635 |
|
|
| Compulsory course material |
| |
R and SAS will be used as softwares in this course. |
|
|
|
|
|
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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