Descriptif

1. Basics on statistics and probability
   • Derivatives and integrals (revision)
   • Some basics on correlation and causation
   • Inferential statistics (population and sample, Weak Law of Large Numbers, Central Limit Theory)
   • Descriptive statistics (measures of central tendency, measures of variation, percentiles…)
   • Experiments, conditional probabilities, random variables, probability density functions for continuous and discrete random variables, characteristics of probability distributions
   • Introduction to estimation (unbiasedness, efficiency, consistency)
   • Exercises

2. Introduction to econometrics
   • What is an econometric model? Variables, parameters
   • Linear models and basic assumptions
   • Exercises

3. R coding
   • Installing R and Rstudio
   • R environment and packages
   • R coding (functions, vectors, dataframes)
   • Importing and exporting data
   • Data management
   • Basic data analysis
   • Graphics
   • Exercises