Model Complications

In this chapter, we will expand our understanding of the linear model to address many issues that the practical researcher must face. We begin with a review and reformulation of the linear model. We then move on to discuss how to address violations of assumptions such as non-linearity and heteroskedasticity (yes, this is a real word), sample design and weighting, missing values, multicollinearity, and model selection. By the end of this chapter, you will be well-supplied with the tools for conducting a real-world analysis using the linear model framework.

Slides for this module can be found here.

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