Principles of Statistical Inference DL (3787) |
| Language of instruction : English |
| Credits: 3,0 | | | | | Period: semester 1 (3sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | 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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Concepts of Probability and Statistics DL (3220)
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5.0 stptn |
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The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.
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This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.
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Distance learning ✔
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Response lecture ✔
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Period 1 Credits 3,00
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Formularium is provided. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Previously purchased compulsory textbooks |
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Mathematical Statistics and Data Analysis,John Rice,Brooks/Cole,9780495118688 |
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| Compulsory course material |
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The R software will be used in this course. |
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| Recommended reading |
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Statistical Inference,G. Casella; R.L. Berger,second,Brooks/Cole Cengage Learning,9780534243128
An introduction to mathematical statistics,Bijma, Jonker and Van Der Vaart,Amsterdam University Press,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=5046611&pq-origsite=summon |
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Learning outcomes Master of Statistics and Data Science
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- EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.
| - EC
| The student is capable of acquiring new knowledge. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
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second year Data Science - distance learning
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J
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second year Master Bioinformatics - distance learning
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J
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second year Master Biostatistics - distance learning
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J
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second year Quantitative Epidemiology - distance learning
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J
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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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