First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
STATISTICS 2 (Ult. numero di matricola pari)
SCP4063587, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
SC2095, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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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
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction Italian
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

Teacher in charge GIOVANNA MENARDI SECS-S/01
Other lecturers PAOLO GIRARDI

Course unit code Course unit name Teacher in charge Degree course code
SCP4063587 STATISTICS 2 (Ult. numero di matricola pari) GIOVANNA MENARDI SC2094

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 of
Individual study
Practice 2.0 28 22.0 2
Lecture 10.0 80 170.0 No turn

Start of activities 01/10/2018
End of activities 18/01/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
6 Commissione a.a 2018/19 (matr.pari) 01/10/2018 30/09/2019 MENARDI GIOVANNA (Presidente)
VENTURA LAURA (Membro Effettivo)
5 Commissione a.a.2018/19 (matr.dispari) 01/10/2018 30/09/2019 ADIMARI GIANFRANCO (Presidente)
MENARDI GIOVANNA (Membro Effettivo)
VENTURA LAURA (Membro Effettivo)
3 Commissione a.a.2017/18 (matr. dispari) 01/10/2017 05/11/2018 ADIMARI GIANFRANCO (Presidente)
CANALE ANTONIO (Membro Effettivo)
SARTORI NICOLA (Membro Effettivo)
VENTURA LAURA (Membro Effettivo)

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 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 Luigi, Alessandra Salvan, Introduzione alla Statistica. Padova: Cedam, 2001. Cerca nel catalogo
  • Azzalini Adelchi, Inferenza Statistica. Una presentazione basata sul concetto di verosimiglianza. Milano: Springer, 2001. Cerca nel catalogo
  • Piccolo Domenico, Statistica per le decisioni. Bologna: Il Mulino, 2010. Cerca nel catalogo
  • Cicchitelli Giuseppe, Statistica: principi e metodi. Milano: Pearson, 2012. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Interactive lecturing
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)