Sumários

Lecture 7

13 Março 2026, 10:30 • RUI PAULO

Estimation of the vector of regression coefficients via maximum likelihood. Estimation of phi.

Lecture 6

6 Março 2026, 10:30 • RUI PAULO

Unit deviance. Dispersion form of an exponential dispersion model. The saddlepoint approximation. Scaled total deviance and its approximate distribution.

Lecture 5

27 Fevereiro 2026, 10:30 • RUI PAULO

Comparing nested models: sequential anova table. Diagnostics. Generalized linear models: random and systematic structure. The exponential dispersion family: definition, examples, properties.

Lecture 4

20 Fevereiro 2026, 10:30 • RUI PAULO

Inference in the context of a linear multiple regression model: marginal significance tests and confidence intervals for the regression coefficients. Confidence intervals for the predicted values. Comparing nested models (to be continued.)

Lecture 3

13 Fevereiro 2026, 10:30 • RUI PAULO

Weighted multiple linear regression. Matrix formulation of the model and of the weighted least squares criterion. Weighted least squares estimates of the regression coefficients. Properties. Decomposition of the total variation. Adjusted R-square. Estimating the variance of fitted values. Example of fitting a multiple linear regression in R.


Interpretation of the coefficients of a multiple linear regression model: quantitative variable and qualitative variable. Interpretation when the response is the log of the variable of interest.