Modelos Lineares Generalizados e Aplicações
To understand and to be able to use binary, Poisson, gamma, log-linear regression models, framing them into the vaster class of models, the generalized linear models.
To know each of the models assumptions and to be able to say that they are met in practice.
To estimate the models using a specific software (R-project), to know how to interpret the estimates that are obtained and to assess the quality of the fits.
To study discrepant and influential observations and know how to deal with them.
To analyze several practical examples, mainly real, using the proposed methodologies.
Inês Jorge da Silva Sequeira
Weekly - 4
Total - Available soon
Knowledge of Simple and Multiple Linear Regression Analysis.
Cordeiro GM, Demétrio CGB. (2008). Modelos Lineares Generalizados e Extensões. http://www.lce.esalq.usp.br/arquivos/aulas/2010/LCE5868/livro.pdf
Dobson AJ (1990). An Introduction to Generalized Linear Models. Chapman & Hall/CRC
Venables WN, Smith DM, R Core Team (2014). An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.1.0. http://www.r-project.org/
Hosmer DW, Lemeshow S (2013). Applied Logistic Regression – 3rd Edition. Wiley
McCullagh P, Nelder JA (1989). Generalized Linear Models - 2nd Edition. Chapman & Hall/CRC
Rodríguez, G. (2007). Lecture Notes on Generalized Linear Models. http://data.princeton.edu/wws509/notes/
Turkman MAA, Silva GL (2000). Modelos Lineares Generalizados - da teoria à prática. SPE http://docentes.deio.fc.ul.pt/maturkman/mlg.pdf
Two moments of evaluation: individual report (50%) and final test (50%).
UA 1 – Linear Regression Model
UA 2 – Generalized Linear Models – definition, estimation, residuals, fit quality assessment, discrepant and influent observations
UA 3 – Gamma regression model and appications
UA 4 – Binary regression model (logistic) and applications
UA 5 – Poisson regression model and applications
UA 6 – Log-linear model for two-dimensional tables and applications
Programs where the course is taught: