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
Dispari
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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. Cerca nel catalogo
  • Cicchitelli, G., Statistica: principi e metodi. --: Pearson, 2012. Cerca nel catalogo
  • Piccolo, D., Statistica per le decisioni. --: Il Mulino, 2010. Cerca nel catalogo