Study Programmes 2016-2017
| MATH0067-1 | |||||||||||
| Introduction to statistics and probability | |||||||||||
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Duration :
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| 20h Th, 25h Pr, 15h Proj. | |||||||||||
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Number of credits :
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Lecturer :
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| Mario Cools, Vincent Denoël | |||||||||||
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Coordinator :
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| N... | |||||||||||
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Language(s) of instruction :
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| French language | |||||||||||
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Organisation and examination :
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| Teaching in the first semester, review in January | |||||||||||
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Units courses prerequisite and corequisite :
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| Prerequisite or corequisite units are presented within each program | |||||||||||
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Learning unit contents :
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| The course covers superficially four aspects of statistics and probabilities: - data collection (survey, inventory, experiment) - descriptive statistics (1-D and 2-D, including linear regression) - elementary probability distributions - inductive statistics (sampling distributions and hypotheses testing) | |||||||||||
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Learning outcomes of the learning unit :
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| Data Collection
- understand the difference between survey and experimentation, as well as their specificities
- use properly the vocabulary
- set up formally an inventory, a survey, an experiment
Descriptives Statiststics - know the different indicators of position and distribution - understand the notions of correlation and stochastic independence - choose adequately the tools for a graphical representation and apply the concepts with Matlab - summarize data to a set of indicators - use (least-squares) linear regression and nonlinear regression Probability - classification of probability distributions - importance of the normal distribution and the central limit theorem - probabilistic modeling with random variables - algebraic operations and other transformations of random variables Inferential Statistics - understand consequences of the choice of a sampling strategy - formalize and test hypotheses |
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Prerequisite knowledge and skills :
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| - Calculus | |||||||||||
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Planned learning activities and teaching methods :
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| The course target the different job opportunities that an architect-engineer may face:
- Design office (descriptive statistics, data interpretation,...)
- Administration (statistics, survey, inventories,...)
- Research or PhD thesis (experimentation, hypothesis testing,...)
The lectures are a mix between ex cathedra theory and exercises. The course ends with a personal project to develop. |
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Mode of delivery (face-to-face ; distance-learning) :
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| Lectures take place on Tuesday morning, 8:30 till 12:30. Attendance is compulsory | |||||||||||
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Recommended or required readings :
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| Pierre Dagnelie, Statistique théorique et appliquée, de Boeck, 2nd édition, 1998. | |||||||||||
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Assessment methods and criteria :
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| 35% Report on a personnal project
65% A written examination concerning theoretical aspects (35%) as well as pratical ones (30%). The final mark is obtained as an arithmetic mean of the marks obtained for each question. The final mark is unique and globalized for both theory and exercises. |
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Work placement(s) :
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Organizational remarks :
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Contacts :
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| Vincent Denoël 04/366.29.30 v.denoel@ulg.ac.be | |||||||||||
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Items online :
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 | Lecture slides click here to download (open access) |
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