OVERVIEW

The course “Statistics 1” aims to provide students with the main tools for the quantitative analysis of economic and social phenomena, and to supply the basic statistical knowledged needed to face the other quantitative courses of the Degree Programme.

AIMS AND CONTENT

LEARNING OUTCOMES

Introductory Statistics. In the course the main topics of descriptive statistics, probability and inferential statistics are discussed.

AIMS AND LEARNING OUTCOMES

Conoscenza e comprensione: Gli studenti conosceranno i principali strumenti per la sintesi dell’informazione e per la generalizzazione di quanto osservato mediante indagini campionarie.

Capacità di applicare conoscenza e comprensione: Gli studenti saranno in grado di riorganizzare i dati in tabelle di frequenza univariate e bivariate fornendone adeguata rappresentazione grafica, effettuare analisi descrittive di base per fenomeni unidimensionali, analizzare le relazioni tra due o più fenomeni con particolare riferimento all’analisi di dipendenza e di regressione lineare. Saranno inoltre in grado di applicare alcuni strumenti di inferenza statistica per risolvere semplici problemi.

Autonomia di giudizio: Gli studenti devono saper utilizzare sia sul piano concettuale che su quello operativo le conoscenze acquisite con autonoma capacità di valutazione e con abilità nei diversi contesti applicativi.

Abilità comunicative: Gli studenti acquisiranno il linguaggio tecnico tipico della disciplina per comunicare in modo chiaro e senza ambiguità con interlocutori specialisti e non specialisti.

Capacità di apprendimento: Gli studenti svilupperanno adeguate capacità di apprendimento che consentano loro di continuare ad approfondire in modo autonomo la materia.

Knowledge and understanding: Students will know the main tools used to summarize the information and for the generalization of the information observed through sample surveys.

Ability to apply knowledge and understanding: Students will be able to reorganize data in univariate and bivariate frequency tables providing adequate graphical representation; carry out basic descriptive analyses for one-dimensional phenomena; analyze the relationships between two or more phenomena with particular emphasis on dependence and linear regression analysis. They will also be able to apply some statistical inference tools to solve simple problems.

Making judgements: Students must be able to use the acquired knowledge both on a theoretical and operational level with autonomous assessment skills, in various applicative contexts.

Communication skills: Students will acquire the technical language typical of the discipline to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Students will develop adequate learning skills that allow them to continue to study the subject independently.

PREREQUISITES

The course requires knowledge of the basic contents of a course of General Mathematics for Business and Economics.

TEACHING METHODS

Traditional lessons and exercises and through AulaWeb. Since the training objectives concern both theoretical and applicative skills, there will be both lessons focused on the methodological aspects of statistics and lessons based on exercises in which numerical problems are faced and examples of simple analyzes on real data are discussed.

Due to the Covid19 pandemic, students will be informed via email and AulaWeb messages on the actual method of the lessons (in person or remotely).

SYLLABUS/CONTENT

Part I: Elements of descriptive statistics

1. Introduction to data collection
2. Distribution of a variable and its representation: Frequency distributions and diagrams
3. Location indices: means, median, mode, percentiles
4. Scale indices
5. Measures of  concentration
6. Index numbers
7. Association measures for two variables
8. Statistical independence
9. Association between qualitative variables.
10. Correlation between quantitative variables
11. Simple linear regression

Part II: Probability

1. Events and events sigma-field
2. Kolmogorov axioms
3. Conditional probability and independence
4. Bayes theorem
5. Discrete and continuous random variables:
   1. Bernoulli and Binomial r.v.
   2. Poisson r.v.
   3. Normal r.v.
6. The Central Limit Theorem

Part III: Introduction to statistical inference

1. Sampling and sampling distributions
2. Point estimation
3. Estimators and their properties
4. Interval estimation
5. Confidence interval for a mean (variance unknown)
6. Confidence interval for a proportion
7. Theory of statistical
8. Test for a mean, test for a proportion.
9. Comparison of two means.

RECOMMENDED READING/BIBLIOGRAPHY

Foreign students are asked to contact the teachers to agree on the text book in English language.

TEACHERS AND EXAM BOARD

Exam Board

FABIO RAPALLO (President)

CORRADO LAGAZIO

LUCA PERSICO

LESSONS

LESSONS START

The course starts on 15 February 2021

Class schedule

STATISTICS 1 A

EXAMS

EXAM DESCRIPTION

The final exam is an oral exam. The complete exam rules are published on the course page on Aulaweb. 

ASSESSMENT METHODS

The oral exam consists of: 

1) questions on theory  

2) questions based on the exercises 

The questions and exercises are chosen in such a way to cover, as far as possible, all the topics of the exam program. Theoretical questions are used to assess the student's degree of understanding, while the questions based on the exercises are used to measure the ability to apply the acquired knowledge. 

Exam schedule

FURTHER INFORMATION

Additional information for non-attending students

For non-attending students, no program changes or assessment procedures are applied. Non-attending students are however invited to contact the teacher.