Study-unit PROBABILITY AND MATHEMATICAL STATISTICS
| Course name | Informatics |
|---|---|
| Study-unit Code | 55007206 |
| Curriculum | Comune a tutti i curricula |
| Lecturer | Alessio Troiani |
| Lecturers |
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| Hours |
|
| CFU | 6 |
| Course Regulation | Coorte 2022 |
| Supplied | 2023/24 |
| Supplied other course regulation | |
| Learning activities | Base |
| Area | Formazione matematico-fisica |
| Sector | MAT/06 |
| Type of study-unit | Obbligatorio (Required) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| Contents | Basic notions of descriptive statistic; linear regression, parametric estimation, confidence intervals, hypothesis testing. Coherence principle. |
| Reference texts | Main references: Iacus S.M., Masarotto G.: Laboratorio di statistica con R. McGraw-Hill. Erto P.: Probabilita' e Statistica per le scienze e l'ingegneria, Mc-Graw-Hill, ed. 2004 R. Scozzafava: Incertezza e Probabilità (Zanichelli). Alternatively: S. Ross, Introduction to probability and Statistics for Engineers and Scientists, Academic Press, 2009. |
| Educational objectives | Knowledge and ability on basic probability, descriptive and inferential statistical notions. Students will be able to face and solve practical and theoretical problems about descriptive statistic, linear regression and hypothesis tests. They will be also able to consciously express the learned notions. |
| Prerequisites | Basic calculus notions, basic notion of Algebra and combinatorics. Basic computer abilty. To fully understand the subjects are recomended the know what is teached in the courses "Analisi Matematica I & II", "Informatica I" |
| Teaching methods | Theoretical lessons on all the subjects and practical exercises developed also with the specific statistical software R |
| Other information | For students with Specific Learning Disorders and/or Disabilities please refer to the we page: http://www.unipg.it/disabilita-e-dsa |
| Learning verification modality | Practical test R to verify the ability to face and solve practical problems of basic statistics and 3 theoretical questions (multiple choice and/or short answer test with full score only in case of correct answer, possible penalty in case of wrong answer) aimed at verifying the mastery of the notions studied. The practical test in R consists of a set of questions (generally between 3 and 6) to be developed on the basis of simulated data or directly provided. It must be carried out within an hour and a half and the various points have indicated the maximum achievable score (normally variable between 3 and 10 depending on the complexity of the analysis required). The number of points for the theoretical questions will be indicated on the text. FOR ATTENDING STUDENTS: there are two partial tests with the same overall assessment methods but on topics only on the first or second part of the course, respectively. The average of the two tests will constitute the final grade. For carrying out the practical test in R, the reference material is mainly the one within the first recommended text (statistics laboratory in R) and the material present in Unistudium. For theoretical questions, reference is made to what is contained in the remaining recommended texts. - On request, the exam can be done in English. |
| Extended program | Descriptive statistics: statistical unitary, frequencies or in classes distributions; graphical representations; mean values: mode, median, arithmetic mean, means “a la Chisini”; means properties; variability indices; quantiles, Boxplots; double empirical distribution: joint, marginal, conditional frequencies, chi-squared dependence index.Liner regression: min-squared estimates; previsions; R2 index. Principal probability distributions: binomial, geometric, Poisson, uniform, exponential, normal. Distributions of sample statistics: chi-squared and t-student. Parametric estimation: main estimators and their properties.Interval estimation: general method; specific cases for the mean and variance of normal populations.Hypothesis testing: generic parametric tests and particular cases with normal populations; non parametric tests: binomial, adaptation an independence tests. |
| Obiettivi Agenda 2030 per lo sviluppo sostenibile |