Project: Multivariate and Hierarchical Data (3565) |
| Language of instruction : English |
| Credits: 8,0 | | | | | Period: semester 2 (8sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
|
Sequentiality
|
| |
|
No sequentiality
|
|
|
Contents "Multivariate and Hierarchical Data": - Repeated measures - Clustered data - Multivariate methods. Contents "Discovering Associations": - Sample size calculations - Statistical research for pharmaceutical research and development - Ethical aspects of consulting, reporting - Statistical consulting training & protocol for the design of experiments
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 8,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 40 % |
|
|
|
|
| Oral evaluation during teaching period | 5 % |
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | All components have to be taken up. |
|
|
|
| Consequences | Students get an X score if they do not meet the condition. |
|
|
|
| Additional information | Note: For the oral part of the exam, the range is [-4;+5]. This penalizes an imbalance between good reports and poor individual performance. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The assignment for the second chance exam remains the same. The
structure of the oral exam for the second chance remains the same. |
|
|
|
|
|
| Compulsory course material |
| |
All course material is made available through BlackBoard, by the lecturers. |
|
|
Learning outcomes Master of Statistics and Data Science
|
- 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 can critically appraise methodology and challenge proposals for and reported results of data analysis. | - 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 oral communicator in their own field. | | | - DC
| The student is an effective 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. | - EC
| The student is capable of acquiring new knowledge. |
|
|
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
|
1st year Master Bioinformatics
|
N
|
|
1st year Master Bioinformatics - icp
|
N
|
|
1st year Master Biostatistics
|
N
|
|
1st year Master Biostatistics - icp
|
N
|
|
1st year Master Data Science
|
N
|
|
1st year Master Quantitative Epidemiology - icp
|
N
|
|
1st year Quantitative Epidemiology
|
N
|
|
Exchange Programme Statistics
|
J
|
|
|
1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
|
|