Politecnico di Torino
Politecnico di Torino
Politecnico di Torino
Academic Year 2016/17
1st degree and Bachelor-level of the Bologna process in Territorial, Urban, Environmental And Landscape Planning - Torino
1st degree and Bachelor-level of the Bologna process in Architecture - Torino
1st degree and Bachelor-level of the Bologna process in Aerospace Engineering - Torino
Teacher Status SSD Les Ex Lab Tut Years teaching
Fontana Roberto ORARIO RICEVIMENTO O2 SECS-S/01 40 20 0 0 12
SSD CFU Activities Area context
SECS-S/01 6 A - Di base Matematica, informatica e statistica
Subject fundamentals
Statistical data analysis and probabilistic models are currently used in many fields of application. The course provides an introduction to some methodologies that will be used by the students in their future professional activities. The methodologies will be applied to real datasets using ad-hoc statistical software.

Expected learning outcomes
The course presents statistical methodologies as a tool to extract information from data. The students will be able to analyze data coming from different fields of application.
Prerequisites / Assumed knowledge
The first year mathematics course (Calcolo) provides the mathematical tools that will be used during the course. Basic computer skills are also required.
1) Descriptive statistics: population, samples and sampling designs (basics) ; qualitative and quantitative variables; pictorial and tabular methods; measures of location; measures of variability; bivariate data; robust statistics (basics).[10h]
2) Probability: definitions of probability; properties of probability; conditional probabiliy, Bayes’ theorem, independence. [12h]
3) Univariate distribution. Random variables; probability distributions for discrete and continuos random variables; expected values; some widely-used random variables. [18h]
4) Statistical inference: sampling distributions; central limit theorem; point estimation (basics); confidence intervals for a population mean and proportion; tests of hypotheses (basics). [10h]
5) Elements of multivariate statistics: principal component analysis; cluster analysis; multiple regression (basics).[10h]
Delivery modes
During tutorials some exercises on the application of statistical methodologies to real data will be solved. In IT-laboratories, statistical software will be used to analyze some datasets. The results of the analysis will be used to prepare short reports.
Texts, readings, handouts and other learning resources
Reference textbook: Elementi di Statistica per le Applicazioni, F. Pellerey, Ed. CELID, Collana "Quaderni di matematica per le scienze applicate".
Some learning material will be made available through the course website.
Suggested readings:
• Rapallo, Fabio e Maria Piera Rogantin. "Statistica descrittiva multivariata." Seconda Edizione, CLUT Editrice, Torino (2003)
• Ross, Sheldon M. Introduzione alla statistica. Apogeo Editore, 2008.
Assessment and grading criteria
The exam is written (2 hours). The exam is divided into two parts. The first part (1.5h) concerns the points 2,3 and 4 (see Contents section). The second part (0.5 h) concerns the points 1 and 5 (see Contents section). The maximum score of the first part is 25/30 while the maximum score of the second part is 8/30. The exam score is the sum of the scores of the two parts. If the score is 31/30 or greater then the exam score is "30 e lode".

Programma definitivo per l'A.A.2016/17

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