Study programme 2019-2020 | Français | ||
Probability and Statistics | |||
Programme component of Bachelor's in Engineering à la Faculty of Engineering |
Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3 |
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Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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UI-B2-IRCIVI-003-M | Compulsory UE | GILLIS Nicolas | F151 - Mathématique et Recherche opérationnelle |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 24 | 24 | 0 | 0 | 0 | 4 | 4.00 | 2nd term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-MARO-005 | Probability | 12 | 12 | 0 | 0 | 0 | Q2 | 50.00% |
I-MARO-007 | Statistics | 12 | 12 | 0 | 0 | 0 | Q2 | 50.00% |
Programme component | ||
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UI-B1-IRCIVI-004-M Engineering Mathematics 2 | ||
UI-B1-IRCIVI-003-M Engineering Mathematics 1 |
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
- Understand and explain the basic concepts of probability and some important results (law of large numbers, Central Limit Theorem);- Solve simple applications implementing these concepts and results in a number of concrete situations (gambling, election polls, queues, fault diagnosis, ...); - Construct an interval containing almost certainly the value of a parameter of the distribution of a variable al- Testing the assumptions made on the value of parameters associated with random variables, and other assumptions such as independence, fit and homogénéit- Establish a linear regression model to an explanatory variable.
Content of UE
notions of probability: random variables and distribution; classical distributions; vectors al independence, correlation, normal distribution; laws of large numbers; central limit theorem, characteristic function, approximation to the binomial distribution by the normal distribution and the Poisson distribution; modeling real situations by probability (eg, queues, Google PageRank).
Notions of statistics: basic descriptive statistics in one and two dimensions (sample frequency, graphics); correlation; regression; independence; estimate average intervals, variance, proportion; regression coefficient; maximum likelihood method; hypothesis testing (population mean, proportion, variance, comparison of means and variances, fit, independence); linear regression model (in one variable).
Prior Experience
Calculus.
Type of Assessment for UE in Q2
Q2 UE Assessment Comments
50% probability, 50% statistics
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
50% probability, 50% statistics
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-005 |
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I-MARO-007 |
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Mode of delivery
AA | Mode of delivery |
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I-MARO-005 |
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I-MARO-007 |
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Required Reading
AA | Required Reading |
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I-MARO-005 | Note de cours - Note de cours de Probabilités - Nicolas Gillis et Marc Pirlot |
I-MARO-007 | Note de cours - Statistiques - Nicolas Gillis et Marc Pirlot |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-MARO-005 | Not applicable |
I-MARO-007 | Not applicable |
Recommended Reading
AA | Recommended Reading |
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I-MARO-005 | Notes d'exercices - Exercices - Jacques Teghem |
I-MARO-007 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-MARO-005 | Sans objet |
I-MARO-007 | Sans objet |
Other Recommended Reading
AA | Other Recommended Reading |
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I-MARO-005 | Not applicable |
I-MARO-007 | Not applicable |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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I-MARO-005 | Authorized |
I-MARO-007 | Authorized |