OVERVIEW

The teaching unit “Statistics” aims to provide the main tools for quantitative data analysis with special attention to the measurement of economic and social phenomena. The skills acquired with this teaching unit are essential for the continuation of studies in subsequent teaching units in the statistical and quantitative area; they also constitute a fundamental tool for understanding the statistical analyses introduced in other disciplines of the Degree Programme.

AIMS AND CONTENT

LEARNING OUTCOMES

The main aim of the course is to provide the fundamental tools of descriptive and inferential statistical analysis. The skills that must be acquired at the end of the course concern: mastery of the fundamental concepts of univariate and bivariate descriptive statistics for the collection and synthesis of data; knowledge of the basic elements of probability theory, in order to allow understanding of the fundamental concepts of statistical inference; the acquisition of the main statistical inference techniques (in particular theory of point and interval estimation and hypothesis testing); the mastery of some statistical models for the analysis of statistical relationships (dependence and independence, correlation, regression) from both a descriptive and inferential point of view. The skills acquired also include the ability to read and understand statistical analyses, as well as to produce correct data analyses in simple application contexts.

AIMS AND LEARNING OUTCOMES

The learning objectives that will be evaluated for the purpose of passing the final exam are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics; knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation, hypothesis testing and regression analysis problems.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; be able to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to carry out dependence/independence and regression analyses, also in the inferential context; know how to read statistical analyses carried out with the methodologies presented in the teaching unit.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the teaching unit.

PREREQUISITES

No prerequisites are required beyond the content of the preparatory teaching unit in General Mathematics.

TEACHING METHODS

Classroom lessons and exercises, both traditional and via the AulaWeb platform. Since the training objectives concern both theoretical and applicative skills, the lessons focused on methodological aspects of statistics will be alternated with exercises in which numerical problems and examples of simple analyses on real data are addressed.

Students with certified disabilities, specific learning disorders (SLD), or special educational needs must contact, at the beginning of the lessons, both the instructor and the Department's disability liaison, Professor Serena Scotto (scotto@economia.unige.it), to agree on teaching and examination methods that, in compliance with the objectives of the teaching unit, take into account individual learning methods and allow the use of any compensatory tools.

Attendance is not compulsory, but highly recommended.

SYLLABUS/CONTENT

Part I: Descriptive statistics

1. Introduction to statistics. Descriptive and inferential statistics. Populations and samples.
2. Distributions, frequencies, and cumulative frequencies. Graphs to describe categorical variables, time series, and quantitative variables. Graphs and tables to describe relationships between variables.
3. Location measures: mean, median, mode, percentiles.
4. Variability measures. Box-plot. Measures of  concentration.
5. Measures of relationships between variables.

Part II: Probability

1. Random experiments, outcomes, events.
2. The probability function and its axioms. Rules of probability.
3. Conditional probability and independence. Bivariate probabilities.
4. Bayes’ theorem.
5. Discrete random variables and their properties.
6. Bernoulli, Binomial, Poisson distributions.
7. Continuous random variables and their properties.
8. Uniform and normal distributions.
9. Joint distributions.

Part III: Inference

1. Sampling and sampling distributions.
2. Distribution of the sample mean. The central limit theorem. Distribution of the sample proportion.
3. Point estimation. Estimators and their properties.
4. Confidence intervals. Confidence intervals for the mean and for the proportions.
5. Confidence interval for a proportion
6. Theory of statistical hypothesis testing. Test for a mean, test for a proportion.
7. Comparison of two means.

Part IV: Relationships between variables

1. Correlations and linear regression. The simple linear regression model. Least squares technique least squares coefficient estimators. The explanatory power of a linear regression equation. Inference on the coefficients.
2. Statistical independence and chi-square test.

RECOMMENDED READING/BIBLIOGRAPHY

"Statistica"; Newbold, Carlson, Thorne, Nona edizione, ed. Pearson (2021). Per gli studenti stranieri è possibile fare riferimento al testo originale in lingua inglese.

"Introduzione alla statistica inferenziale - per le scienze economiche e aziendali"; D. De Martini, ed. Esculapio (2024).

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Lessons begin in the first week of the second semester as per the Department teaching calendar.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of a written test. An additional discussion may be requested at the discretion of the examination committee.

ASSESSMENT METHODS

The written exam consists of:

1) multiple choice theoretical questions

2) open-ended theoretical questions

2) exercises 

The questions and exercises are chosen so as to cover, as far as possible, all the topics of the exam programme. The theoretical questions are used to evaluate the student's level of understanding, while the exercises are used to measure the ability to apply the knowledge acquired.