Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Courses LMAT1121 and LMAT1122 (real analysis/calculus, in particular bivariate integration).
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
At the end of this learning unit, the student is able to :
Contribution of the course to learning outcomes in the Bachelor in Mathematics programme. By the end of this activity, students will be able to:
Recognise and understand a basic foundation of mathematics.
Choose and use the basic tools of calculation to solve mathematical problems.
Recognise the fundamental concepts of important current mathematical theories.
Establish the main connections between these theories, analyse them and explain them through the use of examples.
Show evidence of abstract thinking and of a critical spirit.
Argue within the context of the axiomatic method. Recognise the key arguments and the structure of a proof.
Distinguish between the intuition and the validity of a result and the different levels of rigorous understanding of this same result.
Learning outcomes specific to the course.
The general goal of the course is to introduce the student to the notion and the tools of probability theory and statistical analysis, with a view towards applications. By the end of the course, students will be able to:
Use the basic notions of probabilistic modelling, being able to worki with random variables:
Apply the most frequently used techniques of probability theory (conditional probabilities and expectation, normal, Poisson and exponential laws) in various fields of application
Explore structured data sets by the methods of statistical inference
Apply the techniques of confidence intervals and hypothesis testing
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
First part: Probability
- Events and probabilities
- Conditional probabilities
- Discrete random variables
- Continuous random variables
- Multivariate probability distribution (random vectors)
- Limit theorems (Central Limit Theorem, Law of large numbers)
- Random sampling and descriptive statistics
- Construction of estimators and estimation theory
- Confidence intervals
- Hypothesis testing (for means, variances and proportions)
- Linear regression
The examination tests knowledge and understanding of fundamental concepts and results, ability to construct and write a coherent argument, mastery of the techniques of calculation and, above all, the applicability of the methods covered in the course to problems in the statistical analysis of data.
On the website can be found : copies of transparencies, exercice problems and their solutions, a list of formulas and statistical tables, the help file for using the statistical software, a copy of a recent exam and the detailed table of contents of the course.
D. Wackerly, W. Mendenhall, R. Scheaffer : "Mathematical Statistics with Applications" (7th ed.) 2008, Brooks/Cole.
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