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Course unit
STATISTICAL MODELS 2
SCP4063744, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Educational activities in elective or integrative disciplines |
SECS-S/01 |
Statistics |
9.0 |
Course unit organization
Period |
First semester |
Year |
3rd Year |
Teaching method |
frontal |
Type of hours |
Credits |
Teaching hours |
Hours of Individual study |
Shifts |
Laboratory |
2.0 |
16 |
34.0 |
2 |
Lecture |
7.0 |
48 |
127.0 |
No turn |
Examination board
Board |
From |
To |
Members of the board |
2 Commissione a.a.2019/20 |
01/10/2019 |
30/09/2020 |
SALVAN
ALESSANDRA
(Presidente)
CATTELAN
MANUELA
(Membro Effettivo)
MENARDI
GIOVANNA
(Membro Effettivo)
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Prerequisites:
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Students are assumed to have background knowledge of the following topics covered in undergraduate courses of the Department of Statistical Sciences.
Calculus
Introduction to Probability
Linear Algebra
Statistics 1 and 2
Statistical Models 1 |
Target skills and knowledge:
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The course aims to expand the knowledge of regression models, in particular introducing the students to the theory and applications of generalized linear models. These include models for continuous, binary, categorical, count data. The course also provides the tools necessary for data analysis usig generalized linear models, using the statistical software R.
The students are expected to gain the following skills:
1. Knowledge of the methodology for the specification, inference and evaluation of the various models;
2. Ability to analyze a data set, even with a certain degree of complexity, by specifying, fitting and critically evaluating possible models. |
Examination methods:
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Written exam in computer lab (answer to some exam questions will require the use of the software R).
Detailed exam rules, as well as sample exams with solutions are available at the Moodle page of the course (https://elearning.unipd.it/stat/). |
Assessment criteria:
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Student’s assessment will consider how the topics presented are mastered in problems and applications. |
Course unit contents:
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Generalized linear models (GLM)
- Exponetial families, exponential dispersion families and GLM's: models, moments, link function, likelihood.
- Inference on the parameters of a GLM (point and interval estimation, hypothesis testing).
- Model adequacy: deviance and residuals. Model selection.
- Models for binary data.
- Multinomial models for nominal and ordinal responses.
- Models for count data: sampling schemes, Poisson regression, contingency tables and log linear models.
- Overdispersion with binary and count data: diagnosis, mixture models, beta-binomial and negative binomial regression.
- Models for zero-inflated count data.
- Inference based on estimating equations and quasi-likelihood.
- Models for correlated responses: marginal models, multivariate normal responses, generalized estimating equations, generalized linear mixed models. |
Planned learning activities and teaching methods:
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Lectures (48 hours) and Computer Lab (16 hours) classes. |
Additional notes about suggested reading:
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Lecture notes (including material for Labs) are available on the course website.
Further references are listed below. |
Textbooks (and optional supplementary readings) |
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Agresti, A., Foundations of Linear and Generalized Linear Models. Hoboken: John Wiley & Sons Inc, 2015.
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Dobson, A. and Barnett, A., An Introduction to Generalized Linear Models, Third Edition. Boca Raton, FL: Chapman and Hall/CRC, 2008.
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Madsen, H. and Thyregod, P., Introduction to General and Generalized Linear Models. Boca Raton, FL: Chapman and Hall/CRC, 2010.
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Azzalini, A., Inferenza Statistica: una Presentazione basata sul Concetto di Verosimiglianza. Milano: Springer-Italia, 2001. Capitolo 6 (in English: Azzalini, A. (1996). Statistical Inference, based on the Likelihood. Chapman and Hall, Chapter 6)
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Pace, L., Salvan, A., Introduzione alla Statistica - II. Inferenza, Verosimiglianza, Modelli. Padova: Cedam, 2001. Capitoli 8 e 10
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Bortot, P., Ventura, L., Salvan, A., Inferenza Statistica: Applicazioni con S-Plus e R. Padova: Cedam, 2000. Capitolo 5
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Innovative teaching methods: Teaching and learning strategies
- Lecturing
- Laboratory
- Case study
- Problem solving
- Loading of files and pages (web pages, Moodle, ...)
Innovative teaching methods: Software or applications used
- Moodle (files, quizzes, workshops, ...)
- visual presenter
Sustainable Development Goals (SDGs)
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