This course is the third part of an education program in Statistics. It is based on the introductory courses to Statistics of the first two semesters. The content and the difficulty degree of this introductory course match with the actual international level.

The course is a formal introduction to Inferential Statistics:

1. Probability (Concepts and definitions; conditional probability and independence)
2. Random variables and distribution functions (Random variables; random vectors; independence; function of a random variable)
3. Moments, correlation, conditional expectations (Mathematical expectation and variance; moments; covariance and correlation; expectation, variance, and covariance of random vectors; mean and absolute squared errors and prediction; conditional expectations)
4. Theoretical distributions (Discrete distributions; continuous distributions; approximation of distributions; multivariate distributions; continuity correction)
5. Random samples and limit theorems (Random samples; limit theorems)
6. Point estimation (Estimator; maximum likelihood method; estimation by interval)
7. Statistical inference (Hypotheses and statistical tests; power function and types of error; test on the mean: the t-test)

The theoretical concepts are illustrated by examples, and numerous exercises are provided on the Moodle's platform related to the course

Moodle

The course aims to give students the background to follow without problem an introductory course in Econometrics as well as Master courses in Econometrics and Statistics. The course will be undoubtedly useful to the understanding of other advanced courses supplied by the Faculty.

A script with a listing of references is provided. The student will find on the course's Moodle platform other resources.