Probabilités et statistique

MATH-233

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Summary

A first course in probability and statistics

Content

Motivation: random experiment; probability space; conditional probability; independence.

Random variables: basic notions; density and mass functions; examples; mean, variance,  correlation and covariance; joint distributions, conditional and marginal distributions; transformations.

Notions of convergence; laws of large numbers; central limit theorem; delta method; applications.

Descriptive statistics: basic graphs and statistics; notions of robustness.

Statistical inference: hypothesis testing; types of point estimator and their properties; confidence intervals; likelihood inference; simple examples. 

Learning Prerequisites

Required courses

Analyse I, II, Algèbre linéaire

Teaching methods

Ex cathedra lectures and exercise classes.

Assessment methods

Mid-term test, final exam

Supervision

Office hoursNo
AssistantsYes
ForumYes

Resources

Bibliography

- Dalang, R. C. et Conus, D. (2008). Introduction à la théorie des probabilités. PPUR: Lausanne. 

- Davison, A. C. (2003). Statistical Models. Cambridge University Press: Cambridge. Sections : 2.1, 2.2;  3.1, 3.2; 4.1-4.5; 7.3.1; 11.1.1, 11.2.1. 

- Morgenthaler, S. (2014). Introduction à la statistique, 4ème édition. PPUR: Lausanne. 

















16 December - 22 December


Past Tests and Exams


Additional Exercises


Some Links

Random exercise generator
Random exercise generator

R and RStudio
R-project
- Comprehensive R Archive Network (CRAN)
- RStudio

Visualisations

Confidence intervals
Frequentist inference
Seeing theory

Useful websites

Statistics Tools for Internet and Classroom
Web Pages that Perform Statistical Calculations

Gallery of Data Visualization

Visual Trumpery (talk by Alberto Cairo)

NIST/SEMATECH e-Handbook of Statistical Methods

Rice Virtual Lab in Statistics
Virtual Laboratories in Probability and Statistics
Statoo Consulting's Statistical Links
Professor Risk
The Joy of Stats
History of Mathematics
Statistics and Probability Encyclopedia
Getstats
Straight statistics
Significance
Why learn math?
Jelly beans and multiple testing
Machine Learning

Hal Varian on Statistics and the Future

200 Countries, 200 Years, 4 Minutes