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Program

EN IT

Updated A.Y. 2020-2021

1. Descriptive Statistics and data analysis

(a) Data structures and sources, variables and their measurement scales
(b) Tables and plots: frequency distributions and graphical representation of data
(c) Measures of central tendency and dispersion.
(d) Measures of association of two variables

2. Probability theory

(a) Basic concepts and set theory. Definition of probability, axioms and theorems. Conditional probability and independence. Bayes' theorem
(b) Random variables and probability distributions. Discrete random variables, continue random variables, multiple random variables.  

 

LEARNING OUTCOMES: The course provides the fundamental theoretical notions of the statistical analysis and illustrates the basic techniques for organizing, summarizing and graphically representing ungrouped data. Basics of statistical inference and correlation analysis (ANOVA, linear and multiple regression model) will also be illustrated.


KNOWLEDGE AND UNDERSTANDING: The students remember and understand the fundamental theoretical notions of the analysis of any type of statistical data, including summarizing, organizing and graphically representing the data with the most suitable measures and graphs. They will also be able to formulate and conduct simple hypothesis testing and to set basic regression analysis on small datasets. 


APPLYING KNOWLEDGE AND UNDERSTANDING: The students will be able to produce and interpret basic statistical analyses. The use of examples based on real data (taken from Bank of Italy, Eurostat, Istat, NBER, etc…)  is focal throughout the entire course. The objectives of the course, thus, include the ability of recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, the ability of remembering and understanding the basics of statistical inference as well as of conducting a correct correlation analysis, via the estimation of linear regression models.


MAKING JUDGEMENTS:  The students will be able to autonomously evaluate which, among the several statistical tools acquired, is the most appropriate for the problem at hand. They will be able to formulate statistical hypotheses and to test them with suitable data, properly found among the tons of data available to date. Finally, they will be able to set up a linear regression analysis, evaluating the reliability and suitability of the model based on the estimation output.