| 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
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 |
| Start of activities | 30/09/2019 |
|---|---|
| End of activities | 18/01/2020 |
| Show course schedule | 2019/20 Reg.2014 course timetable |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 8 Commissione a.a.2019/20 (matr.pari) | 01/10/2019 | 30/09/2020 |
MENARDI
GIOVANNA
(Presidente)
ADIMARI GIANFRANCO (Membro Effettivo) FERRACCIOLI FEDERICO (Membro Effettivo) |
| 7 Commissione a.a.2019/20 (matr.dispari) | 01/10/2019 | 30/09/2020 |
ADIMARI
GIANFRANCO
(Presidente)
DALLA VALLE ALESSANDRA (Membro Effettivo) MENARDI GIOVANNA (Membro Effettivo) |
| 6 Commissione a.a 2018/19 (matr.pari) | 01/10/2018 | 31/10/2019 |
MENARDI
GIOVANNA
(Presidente)
ADIMARI GIANFRANCO (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 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) |
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