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Updated A.Y. 2023-2024

Updated A.Y. 2023-2024
Course Program 

The course is structured into the following sub-parts:

Part I: Descriptive Analysis
Introduction to Statistics
Types of data and variables
Summarizing and graphic data

Part II: Probability
Experiments, outcomes, sample spaces and probability axioms
Marginal and conditional probability
Independent events
Main probability distributions: Bernoulli, Binomial, Normal, Chi-Square, t-statistics, F

Part III: Inference
Hypothesis testing

Part IV: Regression analysis
Simple and multiple linear regression model: Assumptions and properties

This module (Module I) will cover Part I and Part II. A detailed Syllabus is available on the course's website.

Course Methodology

The course offers three weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing via examples and exercises.  Some lectures will be devoted to practice with exercises and problems.


The main textbook is Introductory Statistics (8th Edition), by P. S. Mann, edited by Wiley.

Slides presented in class and additional material will be also provided on the course's website.

Exam  structure
The written exam covers the whole program of the course.
It includes multiple-choices as well as open questions, on both theoretical notions and applied issues, featuring e.g. graphs, computation of descriptive statistics and  estimation outputs applied to small datasets. This will allow to evaluate the student in terms of understanding and interpretation of the final results of an basic statistical analysis. 
Final mark ranges between 18 (minimum to pass the exam) and 30 (maximum mark).

Exam rules

There will be no mid-term exam
Students must book via Delphi for the exam. Please notice that students not booked on Delphi will not be allowed to take the exam.
The exam can be taken only once per session.
The students are allowed to bring with them in the exam room pens, calculator, and ID document. Textbooks, notes, smartphones or any other material are not allowed and their use by the students will invalid the exam. In such an event, the student will be allowed to re-sit the exam during the following session.
Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.
Course objectives (5 Dublin Descriptors)

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 will know the fundamental theoretical notions of the analysis of any type of statistical data, including those aimed at 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.

Communication Skills
The students will be able to use data to get indications about economic phenomena and to effectively summarize and communicate them, also via e.g. graphical representations. Moreover, they will be able to produce short reports describing the research question, the statistical hypotheses to be tested, the dataset used (highlighting the nature of the dataset, potential outliers, ect) and the statistical tools used for the analysis. They will also be able to produce, interpret, evaluate and compare linear regression models.

Learning Skills
The course endows the students with an operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labour market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.