Contents of the Course

- General introduction to the statistical method.
- Preliminary definitions and glossary.
- Type of statistical characters and their properties.
- Tables and frequency distributions.
- Graphical representations.
- Measures of centrality and dispersion.
- Contingency tables, independence and measures of association.
- Correlation and linear regression.
- Probability: definitions, basic concepts and theorems.
- Conditional probability. Independence.
- Random variables (discrete and continuous), probability and distribution functions and expected values. Linear combinations of random variables.
- Sampling and sampling distributions.
- Pointwise estimation and interval estimation for the mean, for the variance and for the proportion of a population. 
- Hypothesis testing for the mean, for the variance and for the proportion of a population.

Learning outcomes

At the end of the course the students will be able to:

1. address and propose solutions to simple problems of economic nature on the basis of the information contained in the available data and duly summarized through appropriate statistical analysis techniques;
2. be able to analyze a data set  with the software R.

Pre-requirements

The course holds almost everywhere an introductory level and requires only the knowledge of a basic course in Mathematics.

Teaching methods

The course consists of lectures in lab mode with discussion of techniques of analysis, examples of practical analysis and contextual applications on real data.

Final assessment

The final exam consists in solving theoretical and applied exercises supported by the use of the softare R.

Bibliography

Schmuller, J. (2017). Statistical Analysis with R For Dummies. John Wiley & Sons, Inc.,  Hoboken, NJ 07030-5774. ISBN: 978-1-119-33706-5.
Dalgaard, P. (2008). Introductory Statistics with R (II ed.). Springer Science+Business Media, LLC, New York, NY 10013. ISBN: 978-0-387-79053-4