Generalized Linear Models and Nonparametric Regression

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Atte

• Describe how to generalize the linear model framework to accommodate data that is not suitable for the standard linear regression model.
• State some advantages and disadvantages of (generalized) additive models.
• Describe how an additive model can be generalized to incorporate non-normal response variables (i.e., define a generalized additive model).