Teacher(s)
Language
French
Learning outcomes
At the end of this learning unit, the student is able to : | |
| The fundamental purpose of statistics is to derive results from a sample that are valid for the entire population. This inductive approach is called "Statistical Inference". In a preliminary stage, the sample must be simplified by representing it, without losing too much information, by means of graphs and tables that are as adequate as possible and reducing it to a few numbers that describe it. This is the role of Descriptive Statistics which constitutes the first part of the course. In order to go beyond the simple description of the sample and to draw valid conclusions about the underlying population, one must make additional hypotheses about the way in which the sample data were generated; this is the role of Probability Theory, which provides this indispensable tool for any inferential approach. This inductive approach introduces uncertainty; probability theory also allows us to attach a measure of reliability to any inferential conclusion. The second part of the course will therefore be an introduction to probability. The aim of the first part of the course is to familiarize students with the first tools of Descriptive Statistics; tools which they are confronted with on a daily basis, if only because of the media, which makes great use of them. Inaddition to its interest in describing a state of affairs or a sample, Descriptive Statistics provides an easy introduction to Probability Theory. The second part of the course aims at introducing the probabilistic reasoning mode. At the end of this course, students should be sufficiently comfortable in understanding and manipulating Descriptive Statistics and Probability (simple) to be able to take the Applied Statistics course in BLOC2; Descriptive Statistics and Probability are the first steps and are treated as such. |
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Content
The primary course material is the reference textbook "Notions de statistique", 3rd edition (green cover), written by Christiane Simard.
The chapters covered from this book are Chapters 1 through 3:
Additional notes and slides prepared directly by your professor will also be posted online and will form part of the course. They cover so-called "mathematical" statistics, including:
The chapters covered from this book are Chapters 1 through 3:
- Descriptive statistics
- Probability (terminology, basic properties, and combinatorics)
- Probability distributions (binomial distribution, Poisson distribution, normal distribution)
Additional notes and slides prepared directly by your professor will also be posted online and will form part of the course. They cover so-called "mathematical" statistics, including:
- Study of the fundamental mathematical properties of random variables (cumulative distribution function, probability density function, expected value, variance, exponential distribution)
- Two-dimensional (bivariate) discrete variables
- Any other material the professor decides to cover
Teaching methods
Lectures and practical sessions (exercise sessions) are conducted in person;
a) Lectures provide a systematic introduction to the methodological foundations of descriptive statistics and the theoretical foundations of probability; they are accompanied by examples designed to illustrate this theory. A dedicated effort is made throughout lectures and practical sessions to actively involve students in the development and discovery of new concepts and their applications.
b) Practical sessions are based on the numerous exercises drawn from the course's reference textbook.
c) An active, engaged attitude during lectures and practical sessions, as well as participation in any Q&A sessions, is essential; your chances of success depend on it. Consistent independent work (specifically, working through the solutions to the assigned exercises) is strictly required starting from the very first week of class. It is vital that students establish the rhythm of the course right from the start of the semester by attending lectures and practical sessions every week.
Each student must therefore dedicate sufficient personal study time to ensure they understand and master the material, utilizing the lectures, the reference textbook, and the instructional tools provided for practical sessions. At the end of the semester, the period leading up to the exam should not be a time of first discovery, but rather a review of material that has already been understood and assimilated.
The expected independent study is under no circumstances rote memorization. What will be assessed on the exam is not the student's ability to recite facts, but rather their deep understanding of the concepts and underlying mechanisms, and their ability to apply them properly.
a) Lectures provide a systematic introduction to the methodological foundations of descriptive statistics and the theoretical foundations of probability; they are accompanied by examples designed to illustrate this theory. A dedicated effort is made throughout lectures and practical sessions to actively involve students in the development and discovery of new concepts and their applications.
b) Practical sessions are based on the numerous exercises drawn from the course's reference textbook.
c) An active, engaged attitude during lectures and practical sessions, as well as participation in any Q&A sessions, is essential; your chances of success depend on it. Consistent independent work (specifically, working through the solutions to the assigned exercises) is strictly required starting from the very first week of class. It is vital that students establish the rhythm of the course right from the start of the semester by attending lectures and practical sessions every week.
Each student must therefore dedicate sufficient personal study time to ensure they understand and master the material, utilizing the lectures, the reference textbook, and the instructional tools provided for practical sessions. At the end of the semester, the period leading up to the exam should not be a time of first discovery, but rather a review of material that has already been understood and assimilated.
The expected independent study is under no circumstances rote memorization. What will be assessed on the exam is not the student's ability to recite facts, but rather their deep understanding of the concepts and underlying mechanisms, and their ability to apply them properly.
Evaluation methods
Assessment will take place in June and August, in person; it will be a closed-book, paper exam using a basic calculator (single-line display) and no formula sheet. Solved exam examples from previous years will be available on the course's Moodle page starting from the first lecture. Part of the exam will consist of multiple-choice questions.
This format of examination assesses the student's mastery of the technical and computational aspects of the course, their rigor in the subject, their ability to interpret obtained results, and their reasoning skills.
Such an evaluation tests not only the student's understanding of the course material (understanding its concepts, applying them in exercises, and interpreting the results), but also requires the ability to independently go somewhat beyond the material covered in lectures and practical sessions. In other words, students must make the effort to truly internalize the course material so they can actively apply it.
The June and/or August exams will under no circumstances be identical replicas of past exams. Relying solely on past exams is not sufficient to pass this course.
The use of AI during the exam is strictly prohibited.
This format of examination assesses the student's mastery of the technical and computational aspects of the course, their rigor in the subject, their ability to interpret obtained results, and their reasoning skills.
Such an evaluation tests not only the student's understanding of the course material (understanding its concepts, applying them in exercises, and interpreting the results), but also requires the ability to independently go somewhat beyond the material covered in lectures and practical sessions. In other words, students must make the effort to truly internalize the course material so they can actively apply it.
The June and/or August exams will under no circumstances be identical replicas of past exams. Relying solely on past exams is not sufficient to pass this course.
The use of AI during the exam is strictly prohibited.
Bibliography
- "Notions de statistique", 3ème édition (couverture verte), ouvrage rédigé par Christiane Simard <= la principale référence du cours
- Comte M. et J. Gaden, Statistiques et Probabilités pour les sciences économiques et sociales, Collection Mayor, PUF, 1ère édition, 2000.
- Wackerly D. D., Mendenhall W and R.L. Scheaffer, Mathematical Statistics with Applications, Duxbury Press, 7th ed., 2008.
- Mendenhall W, Beaver R. J. and B. M. Beaver, Introduction to Probability and Statistics, Duxbury Press, 14 ed. 2012.
- Ross S. M., Initiations aux Probabilités, traduction de la 4ème édition américaine, Collection : Enseignement des Mathématiques, Presses polytechniques et universitaires normandes.
- Ross S., A first course in Probability, Pearson International Edition, 9th ed., 2013. ISBN-10: 1292024925.
- Howell D. C., Statistique en Sciences Humaines (M. Rogier, traduction française), Edition Deboeck, 2008.
Il existe une panoplie de livres en français comme en anglais qui reprennent cette matière de base de la statistique et qui sont assortis d’exercices résolus ou non. Certains se trouvent à la bibliothèque ESPO de l’université. De plus, Internet regorge de cours de statistique, plus ou moins poussés, et de documents expliquant des concepts ou des chapitres de ce cours. Une série de vidéos sur YouTube issues de la chaine "Chat Sceptique", permettent une approche aussi sérieuse que ludique de certains concepts statistiques. À certaines occasions, elles seront utilisées au cours-même.
- Comte M. et J. Gaden, Statistiques et Probabilités pour les sciences économiques et sociales, Collection Mayor, PUF, 1ère édition, 2000.
- Wackerly D. D., Mendenhall W and R.L. Scheaffer, Mathematical Statistics with Applications, Duxbury Press, 7th ed., 2008.
- Mendenhall W, Beaver R. J. and B. M. Beaver, Introduction to Probability and Statistics, Duxbury Press, 14 ed. 2012.
- Ross S. M., Initiations aux Probabilités, traduction de la 4ème édition américaine, Collection : Enseignement des Mathématiques, Presses polytechniques et universitaires normandes.
- Ross S., A first course in Probability, Pearson International Edition, 9th ed., 2013. ISBN-10: 1292024925.
- Howell D. C., Statistique en Sciences Humaines (M. Rogier, traduction française), Edition Deboeck, 2008.
Il existe une panoplie de livres en français comme en anglais qui reprennent cette matière de base de la statistique et qui sont assortis d’exercices résolus ou non. Certains se trouvent à la bibliothèque ESPO de l’université. De plus, Internet regorge de cours de statistique, plus ou moins poussés, et de documents expliquant des concepts ou des chapitres de ce cours. Une série de vidéos sur YouTube issues de la chaine "Chat Sceptique", permettent une approche aussi sérieuse que ludique de certains concepts statistiques. À certaines occasions, elles seront utilisées au cours-même.
Teaching materials
- "Notions de statistique", 3ème édition (couverture verte), ouvrage rédigé par Christiane Simard.
Faculty or entity
