OVERVIEW

The course aims to introduce the basic concepts of descriptive statistics and probability, which are essential for performing statistical inference.

AIMS AND CONTENT

LEARNING OUTCOMES

The course provides knowledge to understand the basic definitions of statistics and probability, to understand the difference between a deterministic and statistical approach, to understand the notion of a random variable and to be able to use probability to pass from descriptive statistics to analysis. data through inferential statistics. The student acquires knowldge to build simple statistical-probabilistic models (possibly adapting classical schemes) and discuss the results given by the models

AIMS AND LEARNING OUTCOMES

The expected learning outcomes require the student to be able to handle the basic definitions of statistics and probability, to understand the difference between a deterministic and statistical approach, to have acquired the notion of a random variable and to be able to use probability to pass from descriptive statistics to analysis. data through inferential statistics. The student must be able to build simple statistical-probabilistic models (possibly adapting classical schemes) and discuss the results given by the models.

TEACHING METHODS

Lectures and frontal exercises, exercise sheets, guided exercises, in itinere self-assessment tests. Attendance at lectures and exercises is strongly recommended.
Working students and students with certified specific learning disorders (SLD), disabilities, or other special educational needs are encouraged to contact the instructor at the beginning of the course to agree on teaching and assessment methods that, while respecting the learning objectives, take into account individual learning styles

SYLLABUS/CONTENT

Probability Definitions classical, a posteriori, axiomatic; conditional probability, independence; Bayes theorem, factorization theorem, law of total probability. Discrete and continuous random variables, distribution and density functions, function of random variable. Expected values, moments and theoretical variances. Joint distributions and conditional laws, covariance and correlation.

Descriptive Statistics Qualitative variables: categorical, ordinal; univariate descriptive: percentages and tables, bar and pie charts, Pareto chart, fashion; bivariate descriptive: row and column profiles. Quantitative variables: position indices (mode, median, mean, percentiles and quartiles), cumulative empirical distributions, boxplot; dispersion indices: range, IQR, variances and standard deviation, coefficient of variation; relationship between two quantitative variables: covariance and correlation, Simpson's paradox; linear regression, regression line, R2 coefficient and residual analysis.

Estimates and estimators. Principles of randomness, distortion, mean square error, efficiency.

Confidence intervals By mean (known / unknown variance, small and large sample sizes), by variance, by the difference of means (independent samples and paired samples). Funnel plot (weather permitting).

Statistical hypothesis tests Introduction, type I and II errors, p-value, level of significance, power. Test for the difference of means: independent samples and paired samples. Test for variance. Chi-squared test for categorical variables (comparison between a known distribution and an observed univariate, comparison between two observed univariate). ANOVA

RECOMMENDED READING/BIBLIOGRAPHY

• Sheldon M. Ross, Introduzione alla statistica, Apogeo Formulario e Soluzioni ad esercizi dispari in http://www.apogeoeducation.com/9788891602671-introduzione-alla-statistica.html
• Jay L. Devore, Probability and Statistics for Engineering and the sciences,
• Duxbury P. Newbold, W.L. Carlson, B. Thorne, Statistica, Pearson

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/10716/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of a written test lasting two hours and consists of solving three/four exercises on the topics covered during the year . To participate in the written test, you must register by the deadline at https://servizionline.unige.it/studenti/esami/prenotazione.

Students with disabilities or specific learning disorders (SLD) are reminded that, in order to request accommodations for exams, they must first upload their certification to the University website at servizionline.unige.it in the "Studenti" section.
The documentation will be verified by the University’s Office for the Inclusion of Students with Disabilities and SLD: https://rubrica.unige.it/strutture/struttura/100111.

Subsequently, students must send an email to the instructor responsible for the exam at least 10 days in advance of the exam date, copying both the School's Inclusion Contact Person for students with disabilities and SLD, and the above-mentioned Office.
The email must include the following information:

- the name of the teaching module

- the exam date

- the student's full name and student ID number

- the compensatory tools and dispensatory measures considered necessary and requested.

The Inclusion Contact Person will confirm to the instructor that the student is entitled to request accommodations and that such accommodations must be discussed and agreed upon with the instructor. The instructor will then confirm whether the requested accommodations can be granted.

Requests must be submitted at least 10 days before the exam date, to allow the instructor sufficient time to evaluate them. In particular, if the use of concept maps is requested for the exam (which must be significantly more concise than those used during study), failure to meet the deadline may result in insufficient time to make any necessary revisions.

For further information on requesting services and accommodations, please refer to the document: Guidelines for requesting services, compensatory tools and/or dispensatory measures, and specific aids.

ASSESSMENT METHODS

The written test is aimed at verifying the student's mastery of calculation techniques and knowledge of the main tools of probability and statistics introduced in the course (random variables, random vectors, functions of random variables, limit theorems, estimators, hypothesis testing) and consists of three exercises consisting of several questions of varying difficulty.   The student must be able to correctly solve the exercises and be able to justify the steps required to obtain the final result and use the correct formalism.

FURTHER INFORMATION

Ask the professor for other information not included in the teaching schedule.