Sumários

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.

Lecture 2

6 Fevereiro 2026, 10:30 • RUI PAULO

Linear regression. Weighted simple linear regression. Motivating example. Weighted least squares estimators and properties. Multiple R-square. Fitting a linear regression in R.

Lecture 1

30 Janeiro 2026, 10:30 • RUI PAULO

Practical aspects of the course.


Data: response and explanatory variables. Numeric explanatory variables and factors. Plotting data. Coding of factors using dummy variables. Statistical models: random and systematic components. Generalized linear models and how they generalize linear regression models.