The learning outcomes of the course are:

For students to understand the nature of regression analysis and to be able to interpret bivariate and multivariate analysis results.

Understand and apply the normal linear regression model, make hypothesis tests and make predictions.

Understand and apply diagnostic tools for residue control.

To propose solutions to correct problems presented by the respective data and which have the consequence of violating the assumptions of the normal linear model.

To know and solve the problems of multicollinearity, heteroscedasticity and autocorrelation.

Know basic modeling principles and be able to apply non-linear models.

Know what quality response models are and apply them where necessary.

Know and apply panel data regression models

Know and apply basic time series concepts and tools and make forecasts.

Know basic regression routines and tools in Excel or R.