Statistics, Finance and Actuarial sciences

VALERIA SAMBUCINI

Learning goals.
The primary objective is to provide students with basic concepts and procedures of descriptive statistics.
At the end of the course the student must be able to design a small census survey and to conduct descriptive analyses of the data through the use of a statistical software.
Knowledge and understanding.
Upon completion of the course, students know and understand the main procedures of descriptive statistics.
They are able to organize data in simple and contingency tables and to synthesize them through graphical representations.
They know and are able to calculate the most important statistical indicators that measure
(a) position, variability and form of simple distributions and
(b) important aspects of the joint distribution of two variables.
Furthermore, they have acquired the notion of statistical model and are able to implement a simple regression model.
Applying knowledge and understanding.
Upon completion of the course, students are able to apply the knowledge acquired, in order to interpret and critically evaluate the results of descriptive analysis.
Making judgements.
Through a large number of exercises on all the topics covered, students develop autonomous judgment skills that allow to identify the most appropriate methods to solve problems of descriptive statistics and to critically interpret the results of the elaborations provided by the software.
Communication skills Students, through the study and the performance of practical exercises, acquire the technical-scientific language of the discipline, that must be properly used both in the written and in the oral examinations.
Learning skills Students who pass the exam will have knowledge of the fundamental notions for the descriptive analysis of data.
They will also be able to implement simple codes to organize data in tables and synthesize them through graphics and/or calculation of important indicators.
Therefore, they have acquired the basics to learn what will be proposed in the subsequent statistical courses.

Students who pass the exam will have knowledge of the fundamental notions for the descriptive analysis of data. They will also be able to implement simple codes to organize data in tables and synthesize them through graphics and/or calculation of important indicators. Therefore, they have acquired the basics to learn what will be proposed in the subsequent statistical course.

The course has no specific prerequisites, except the basic mathematical knowledge provided by high school courses.

Introductory concepts: population, statistical unit and variables. Classification of variables. Tables and graphical representations. Empirical cumulative distribution function. Central tendency: definition, properties, calculation and application. Variability measures: definition, properties, calculation and application. Concentration measures. Shape of the distribution: definition and indices of skewness. Comparisons based on differences and ratios. Bivariate and multivariate distributions. Association measures. Concordance and discordance. Correlation. Simple linear regression.
Theoretical lecture and exercise sessions will be accompanied by a computer lab.

Cicchitelli G., D’ Urso P. e Minoro M. (2022). Statistica : principi e metodi. Pearson Editore (quarta edizione).

Teaching material provided by the teacher (theoretical supplementary notes, exercises with solution, R scripts and data) through the platform moodle (https://elearning.uniroma1.it/)

- D. Piccolo. Statistica per le decisioni - Introduzione alla statistica per l'ambiente R. Il Mulino, 2010.

- S. Borra, A. Di Ciaccio. Statistica - metodologie per le scienze economiche e sociali, (Terza Ed.), McGraw Hill, 2014

- F. Crivellari, Analisi statistica dei dati con R, Apogeo, Milano, 2006.

The course is delivered primarily through face‑to‑face lectures, during which both the traditional blackboard and PDF slides are used. All teaching materials are made available to students on the course’s Moodle page. Lectures focus on presenting theoretical concepts and guiding students through applied examples.
Once a week, a dedicated computer laboratory session is held, aimed at introducing students to the use of the R software. During these sessions, students work directly at the computer and follow practical exercises designed to develop autonomy in using the language and the basic tools for data analysis.

It is strongly advised to attend all lectures of this course. Students who are not able to attend the lectures, are invited to contact the lecturer in advance.

The exam consists of two compulsory parts:
(a) a final written examination, that contains exercises on topics of the course program (some of them require the use of the statistical software);
(b) an oral examination, that can only be taken once the student has passed the written test.
Written and oral examinations are aimed at ascertaining both the acquisition of theoretical concepts and the ability to solve concrete problems.

Sample exam questions are provided on the moodle page, https://elearning.uniroma1.it/course/view.php?id=19966

• Topic 1: univariate descriptive statistics: analysis of a single variable through the construction of frequency distributions, the use of measures of location, variability, and shape, and the production of univariate graphs.
  • Books: Cicchitelli G., D’ Urso P. e Minozzo M. (2022). Statistica : principi e metodi. Pearson Editore. Teaching material provided by the teacher.


• Topic 2: joint analysis of two variables using contingency tables; study of statistical relationships through measures of association, mean dependence, and linear correlation; introduction to the simple linear regression model.
  • Books: Cicchitelli G., D’ Urso P. e Minozzo M. (2022). Statistica : principi e
    
    metodi. Pearson Editore. Teaching material provided by the teacher.