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.
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.
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.
No prerequisites are required beyond the content of the preparatory teaching unit in General Mathematics.
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, Prof.ssa Elena Lagomarsino (elena.lagomarsino@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.
Part I: Descriptive statistics
Part II: Probability
Part III: Inference
Part IV: Relationships between variables
Newbold, Carlson, Thorne, Statistica. Nona edizione. Pearson (2021).
Foreign students can refer to the original version of this book. For a topic not covered by the textbook, documentation will be provided in Aulaweb by the teacher.
Ricevimento: It is possible to arrange a meeting with the lecturer by sending an email to marta.nairuscone@unige.it
MARTA NAI RUSCONE (President)
DANIELE DE MARTINI
FABIO RAPALLO
Classes will start in the first week of the second semester according to the calendar of the Department of Economics.
STATISTICS C
The examination consists of a written test which includes:
1) multiple-choice theoretical questions;
2) open-ended theoretical questions;
3) exercises
The complete examination regulations are published on the course's Aulaweb page before the lessons start.
The questions and exercises of the written test are chosen to cover, as far as possible, all the topics of the exam program. 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. In addition to the degree of understanding and the ability to apply knowledge, the correct use of the technical language of the discipline and the ability to read and correctly interpret statistical analyses constitute evaluation parameters.
Details on how to prepare for the exam and the level of depth of each topic will be illustrated and discussed during the lessons.
For non-attending students, no program changes or assessment procedures are applied.
