Statistics, Finance and Actuarial sciences

STEFANIA GUBBIOTTI

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.

After attending the course, students will know and understand the main procedures of exploratory data analysis (graphical representations, tables, statistical indices). They will also be able to use the statistical software R for data processing and to interpret and evaluate the results. Through study and practical exercises, students will acquire the technical and scientific language of the discipline and the fundamental concepts necessary to understand the content of subsequent courses in the statistical area.

Basic concepts of mathematics and use of PC.

The syllabus is structured in the following three parts.

Part 1 (16 hours): Classification, organization and representation of data.
Topics: Simple distributions; classification of continuous variables; graphical representations.

Part 2 (36 hours): Summary of the main aspects of a distribution.
Topics: main location indices and their properties; main variability indices and their properties; main shape indices and their properties.

Part 3 (20 hours): Relationships between variables.
Topics: multiple distributions; contingency tables; independence and measure of dependence; mean dependence; correlation; regression (simple linear model).

Giuseppe Cicchitelli, Pierpaolo D'Urso, Marco Minozzo (2022) "Statistica: principi e metodi". PEARSON.

Traditional lectures include alternation between theoretical aspects, examples of application (with the use of the R software) and exercises.

Course attendance is warmly recommended.

Admission to final exam is subject to the following two-fold test:
A written exam with standard exercises (duration: about 90 minutes)
A practical test with R (duration: about 30 minutes)
* Half of the written + practical test can be skipped by passing a midterm test (usually beginning of november)
The final exam consists in an oral interview (duration: about 20 minutes) mainly focused on the fundamental concepts of exploratory statistics and on the proofs of the theoretical results illustrated during the course.
Overall these tests allow to assess the acquisition of both theoretical concepts and problem solving skills.
Each of the three parts contributes to the final mark with the following weight:
Written test: 1/3
Practical test: 1/6
Oral exam: 1/2

Examples of questions and exercises that are part of the written exam, of the practical test with R, and of the oral exam are available on the Moodle page: https://elearning.uniroma1.it/enrol/index.php?id=19952

• Classification, organization and representation of data.Topics: Simple distributions; classification of continuous variables; graphical representations.
  • Books: Cicchitelli et al, (2022)


• Summary of the main aspects of a distribution.Topics: main location indices and their properties; main variability indices and their properties; main shape indices and their properties.
  • Books: Cicchitelli et al, (2022)


• Relationships between variables.Topics: multiple distributions; contingency tables; independence and measure of dependence; mean dependence; correlation; regression (simple linear model).
  • Books: Cicchitelli et al, (2022)