The objective of this course is to introduce the fundamentals of inferential statistics (sampling, parametric point estimation, confidence intervals, hypothesis testing), as well as the theory of linear and nonlinear regression. This course enables students to acquire (i) theoretical knowledge of statistical inference and regression, (ii) an intuitive understanding of these concepts, (iii) the mathematical rigor necessary to apply these concepts, and (iv) a practical understanding of the tools and methods used to conduct statistical analyses.
The objective of this course is to introduce the fundamentals of inferential statistics (sampling, parametric point estimation, confidence intervals, hypothesis testing), as well as the theory of linear and nonlinear regression. This course will place particular emphasis on the importance and practical applications of these concepts and the resulting methods in everyday life, as well as on the fundamental aspects of modeling real-world phenomena. By the end of the course, students will be able to perform classical statistical tests (t-test, F-test), analyze linear and nonlinear regression models, and interpret output from the R software.
The purpose of this course is to familiarize students with statistical reasoning as well as linear and nonlinear regression. The course will cover the classic concepts of an introductory statistics course (point estimation, hypothesis testing, and confidence intervals), as well as the following topics: normal, chi-square, Student's, and Fisher-Snedecor random variables; random vectors; simple and multiple linear regression; diagnostics; linear model selection; and nonlinear regression.
Illustration of the course concepts and results, and their application using the R software.
The course material will be presented through lectures. Each lecture will be structured around detailed slides, which will be presented and reviewed during class. All course resources (including the slides and exercise sessions) will be available on Webcampus. Practice sessions will allow students to apply the methods presented in the theoretical course and to become familiar with the statistical software R and the application of regression methods to real-world datasets.
Course assessment will consist of two parts:
A written exam during the exam period, with questions that may focus on knowledge and understanding of the material covered in class and during the practice sessions. This exam may also assess students' ability to understand a problem, analyze statistical data, choose the most appropriate method, and correctly interpret the results obtained.
Continuous assessment. This will consist of assignments to be completed by students; these assignments may be subject to one or more written and/or oral evaluations, depending on the number of enrolled students. The assignments may include, in particular, the analysis of real-world datasets.
The written exam during the exam period and the continuous assessment will be organized according to the instructions provided by the instructor during class sessions.
Slides shown in class. Exercise assignments.
| Training | Block | Credits | Mandatory |
|---|---|---|---|
| Additional teaching units for the master's degree in mathematical sciences (200P) | 1 | 5 | No |
| Additional teaching units for the master's degree in physical sciences (210P) | 1 | 3 | No |
| Bachelor in Mathematics | 2 | 5 | Yes |