The main course objectives are:
1. Understand and apply linear regression for estimation, inference, and diagnostics.
2. Introduce the concept of Generalized Linear Models (GLMs) with a focus on the Exponential family, and develop skills in estimation, inference, and diagnostics for GLMs.
3. Explore logistic regression, along with Multinomial, Ordinal, and Poisson models.
4. Gain knowledge of Generalized Additive Models (GAMs) and become proficient in smoothing methods, penalized splines, estimation, and the selection of smoothing parameters.