| Credits: 7,0 | | Study load hours: 189 | Period: semester 1 (7sp)  |
| Language of instruction: English | | Exam contract: not possible |
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Knowledge of basic concepts from probability and statistics are required, as well as familiarity with matrix algebra and basic R programming and reporting skills.
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This course introduces the student to simple and multiple linear regression models, including analysis of variance, and the basics of Generalised Linear Models. The course starts with an introduction to statistical modelling and then moves to the linear model and from there to the Generlised Linear Model )(GLM). Basic techniques for the analysis of contingency tables are also discussed. For the models included in this course, the following topics are covered: parameter estimation, statistical inference on the parameters, prediction, model selection and model assessment.
The course also focuses on the interpretation of the models and their parameters and the correct use of the models and methods. The student will also learn to perform data analyses with linear models in statistical software (R / SAS) and to correctly report the results of the data analysis.
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| Compulsory course material |
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All course material (course notes, handouts, video lectures and exercises) will be available on Blackboard. |
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| Mandatory software |
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Collective feedback moment ✔
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Distance learning ✔
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Response lecture ✔
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Self-study assignment ✔
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Semester 1 (7,00sp)
| 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) | For the final written exam students can use their own hand calculator, lecture notes, own notes, hand-book, statistical tables. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The student must submit all homework assignments in time. |
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| Consequences | If the student does not submit all homework assignments in time, then the final result of this course will receive code "N" (not all evaluation components completed). |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- 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 software. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student is able to efficiently acquire, store and process data. | | | - DC
| ...maintain provenance of data, analyses and results | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | | | - DC
| The student can explain basic principles regarding ethics and integrity in general. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. |
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| Included in these programmes | Tolerance3 |
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N
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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.
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