CODE 60083 ACADEMIC YEAR 2024/2025 CREDITS 9 cfu anno 2 ECONOMIA AZIENDALE 8697 (L-18) - GENOVA 9 cfu anno 2 ECONOMIA DELLE AZIENDE MARITTIME, LOGISTICA E TRASP. 8698 (L-18) - GENOVA 9 cfu anno 2 SCIENZE ECONOMICHE E FINANZIARIE 11662 (L-33) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR SECS-S/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester SECTIONING Questo insegnamento è diviso nelle seguenti frazioni: A B C PREREQUISITES Propedeuticità in ingresso Per sostenere l'esame di questo insegnamento è necessario aver sostenuto i seguenti esami: Business Administration 8697 (coorte 2023/2024) CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 A CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 B CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 C Maritime, Logistics and Transport Economics and Business 8698 (coorte 2023/2024) CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 A CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 B CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 C Economics 8699 (coorte 2023/2024) CALCULUS FOR UNDERGRADUATED STUDENTS. 41138 2023 Propedeuticità in uscita Questo insegnamento è propedeutico per gli insegnamenti: Economics 8699 (coorte 2023/2024) ECONOMETRICS 24615 TEACHING MATERIALS AULAWEB OVERVIEW The course “Statistics 1” 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 course are essential for the continuation of studies in subsequent courses 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 course. 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 course. PREREQUISITES The course requires knowledge of the basic skills of a course in General Mathematics for Business and/or Economics. 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 who have valid certification of physical or learning disabilities and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Serena Scotto (scotto@economia.unige.it), the Department’s disability liaison. SYLLABUS/CONTENT Part I: Descriptive statistics Introduction to statistics. Descriptive and inferential statistics. Populations and samples. Distributions, frequencies, and cumulative frequencies. Graphs to describe categorical variables, time series, and quantitative variables. Graphs and tables to describe relationships between variables. Location measures: mean, median, mode, percentiles. Variability measures. Box-plot. Measures of concentration. Measures of relationships between variables. Part II: Probability Random experiments, outcomes, events. The probability function and its axioms. Rules of probability. Conditional probability and independence. Bivariate probabilities. Bayes’ theorem. Discrete random variables and their properties. Bernoulli, Binomial, Poisson distributions. Continuous random variables and their properties. Uniform and normal distributions. Joint distributions. Part III: Inference Sampling and sampling distributions. Distribution of the sample mean. The central limit theorem. Distribution of the sample proportion. Point estimation. Estimators and their properties. Confidence intervals. Confidence intervals for the mean and for the proportions. Confidence interval for a proportion Theory of statistical hypothesis testing. Test for a mean, test for a proportion. Comparison of two means. Part IV: Relationships between variables 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. Statistical independence and chi-square test. RECOMMENDED READING/BIBLIOGRAPHY 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. TEACHERS AND EXAM BOARD FABIO RAPALLO Ricevimento: By appointment arranged by email fabio.rapallo@unige.it Exam Board FABIO RAPALLO (President) DANIELE DE MARTINI MARTA NAI RUSCONE LESSONS LESSONS START Classes will start in the first week of the second semester according to the calendar of the Department of Economics. Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION 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. ASSESSMENT METHODS 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. Exam schedule Data appello Orario Luogo Degree type Note 20/12/2024 09:30 GENOVA Scritto 15/01/2025 09:30 GENOVA Scritto 30/01/2025 09:30 GENOVA Scritto 27/05/2025 09:30 GENOVA Scritto 19/06/2025 09:30 GENOVA Scritto 15/07/2025 09:30 GENOVA Scritto 01/09/2025 09:30 GENOVA Scritto FURTHER INFORMATION For non-attending students, no program changes or assessment procedures are applied.