Educational offer - University of Padova

First cycle degree courses	Second cycle degree courses	Single cycle degree courses
School of Science
STATISTICS FOR TECHNOLOGY AND SCIENCE

Course unit
STATISTICS 2 (Ult. numero di matricola dispari)
SCP4063587, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit

Degree course	First cycle degree in STATISTICS FOR TECHNOLOGY AND SCIENCE SC2094, Degree course structure A.Y. 2014/15, A.Y. 2019/20	bring this page with you
Number of ECTS credits allocated	12.0
Type of assessment	Mark
Course unit English denomination	STATISTICS 2
Website of the academic structure	http://www.stat.unipd.it/studiare/ammissione-lauree-triennali
Department of reference	Department of Statistical Sciences
E-Learning website	https://elearning.unipd.it/stat/course/view.php?idnumber=2019-SC2094-000ZZ-2018-SCP4063587-DISPARI
Mandatory attendance	No
Language of instruction	Italian
Branch	PADOVA
Single Course unit	The Course unit can be attended under the option Single Course unit attendance
Optional Course unit	The Course unit can be chosen as Optional Course unit

Lecturers

Teacher in charge	GIANFRANCO ADIMARI		SECS-S/01

Mutuating

Course unit code	Course unit name	Teacher in charge	Degree course code
SCP4063587	STATISTICS 2 (Ult. numero di matricola dispari)	GIANFRANCO ADIMARI	SC2095

ECTS: details

Type	Scientific-Disciplinary Sector		Credits allocated
Basic courses	SECS-S/01	Statistics	12.0

Course unit organization

Period	First semester
Year	2nd Year
Teaching method	frontal

Type of hours	Credits	Teaching hours	Hours of Individual study	Shifts
Practice	2.0	28	22.0	2
Lecture	10.0	80	170.0	No turn

Calendar

Start of activities	30/09/2019
End of activities	18/01/2020
Show course schedule	2019/20 Reg.2014 course timetable

Examination board

Examination board not defined

Syllabus

Prerequisites:	Calculus Linear algebra Introduction to Probability Statistics I
Target skills and knowledge:	The course is intended to provide instruments for inferential data analysis. Some statistical models and the main inferential methods are illustrated. The course supplies basic information for likelihood inference, as a general framework for data analysis.
Examination methods:	Written exam, with theoretical questions and exercises.
Assessment criteria:	The student is expected to learn concepts, instruments and methodology for an appropriate application of the inferential techniques.
Course unit contents:	- Introduction to Statistical Inference. - Population, sample, sample data and inference. Statistical models and parametric statistical models. Empirical control of the statistical model. Empirical distribution function. - Main parametric statistical models. - Discrete statistical models: binomial, negative binomial, multinomial, Poisson. - Continuous statistical models: exponential, gamma, normal and related models. - Inferential procedures. - Point estimation. Parameter, estimate, estimator. Method of moments estimator, least squares estimator. Bias, efficiency and consistency of an estimator. - Confidence intervals and confidence regions. Exact and approximated confidence intervals and confidence regions. - Hypothesis test. Statistical test, significance level, p-value, power. Exact and approximated tests. - Likelihood based inference. - Likelihood function and maximum likelihood estimators (mle). - Maximum likelihood estimation: computational aspects. Observed and expected information. Properties of mle's. Approximate distribution of mle: theory, notable examples, applications. Reparameterizations. - Tests and confidence regions based on mle. Tests and confidence regions based on the log-likelihood ratio and asymptotically equivalents. One-sided version of the log-likelihood ratio test. - Relevant applications.
Planned learning activities and teaching methods:	The course is organized in lectures and exercise group sessions (two groups). Exercise sessions assume an an active involvment of the students.
Additional notes about suggested reading:	The first two references below represent the main material for study. Students can deepen their knowledge with the further references provided. Further possible material will be provided during the classes on the Moodle.
Textbooks (and optional supplementary readings)	Pace, L., Salvan, A., Introduzione alla Statistica: II Inferenza, verosimiglianza, modelli. --: Cedam, Padova, 2001. Azzalini, A., Inferenza statistica, una presentazione basata sul concetto di verosimiglianza. --: Springer Verlag, 2001. Cicchitelli, G., Statistica: principi e metodi. --: Pearson, 2012. Piccolo, D., Statistica per le decisioni. --: Il Mulino, 2010.