Updated A.Y. 2019-2020

*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 linear regression model: Assumptions and properties
Multiple linear regression model: Assumptions and properties

This module (Module II) will cover Part III and Part IV.

Course Methodology

The course offers two weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing via examples and exercises. Besides, one weekly lecture will be specifically 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 final evaluation is based on a closed-book written exam, covering the entire course’s program. It includes open questions, multiple choices questions, and exercises aimed at evaluating the knowledge and understanding of the theoretical as well as applied notions illustrated during the course.

Exam rules

• There will be no mid-term exam
• Students must book for the exam. Please notice that students not booked 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 or 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 (according to the 5 Dublin Descriptors)

Knowledge And Understanding
The course provides the students with the theoretical undergrounds of basic statistical analyses. The course will start with an overview of the descriptive analyses, illustrating for each type of data, the most suitable graphical representations and summary statistics, highlighting points of strength and caveats. Then, the fundamentals of Probability will be given, followed by the presentation of the main features of the mostly used random variables models (Bernoulli, Binomial, Normal, Chi-square, t-Student, F). Then, sampling distributions and hypotheses tests will be illustrated, with specific attention to tests about the mean and variance of the population. Finally, simple linear regression and multiple regression models will be presented, specifying the main assumptions and properties.

Applying Knowledge And Understanding
The students will be able to produce and interpret basic statistical analyses. The use of real data (taken from Eurostat, Istat, NBER, etc…) and of appropriate statistical software is focal all throughout the course classes and lectures. The objectives of the course, thus, include the ability of finding the appropriate dataset, recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, as well as of conducting a correct correlation analysis, via the estimation of linear regression models.

Making Judgements
The students will be able to evaluate which, among the different statistical tools acquired, is/are 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 communicate them, via e.g. graphical representations and summary statistics. 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, etc) 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 a substantial operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labor market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.