# 77290 - Crash Course in Econometrics

### SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda. ## Learning outcomes

The course aims at reviewing statistics and probability in order to provide the students with the basic tools necessary to deal with the topics of the course Applied Econometrics

## Course contents

1. Descriptive statistics. Basic concepts. Mode, Median, Mean, Quantiles. Range, Interquartile difference, Variance, Standard Deviation. Covariance, Correlation. OLS line.
2. Probability and Random Variables. Discrete and continuous random variables: definition, distribution/density function, cumulative distribution function, moments and central moments. Expectation and Variance: definition and properties. Examples: Bernoulli, Uniform, Rectangular, Normal random variables. Properties of Normal random variables. Multiple random variables: marginal, conditional and joint distributions. Covariance and its properties. Linear correlation. Variance and covariance matrix.
3. Random sampling and sample distributions: Chi-square, Student t, F. Sample Mean and ratio of two variances distributions. Use of the tables (Z, Chi-square, Student's t, F). Central limit theorems.
4. Estimation theory. Point estimation. Estimators properties. Statistical inference: confidence intervals (CI) and hypothesis testing (HT). CI and HT for an expectation, a proportion, a variance, a ratio of two variances, a difference between two expectations and two proportions.
5. The simple linear regression model: hypotesis, OLS estimation, confidence intervals and hypothesis testing, goodness of the fit.

Blackboard

## Office hours

See the website of Gian Luca Tassinari