Lecture 13 – Ordinary Least Squares

Content by Joey Gonzalez, Andrew Bray, Suraj Rampure, Ani Adhikari, Deb Nolan

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A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.

	Video	Quick Check
13.0 Announcements
13.1 A quick recap of the modeling process, and a roadmap for lecture.
13.2 Defining the multiple linear regression model using linear algebra (dot products and matrix multiplication). Introducing the idea of a design matrix.		13.2
13.3 Defining the mean squared error of the multiple linear regression model as the (scaled) norm of the residual vector.		13.3
13.4 Using a geometric argument to determine the optimal model parameter.		13.4
13.5 Residual plots. Properties of residuals, with and without an intercept term in our model.		13.5
13.6 Discussing the conditions in which there isn't a unique solution for the optimal model parameter. A summary, and outline of what is to come.		13.6
13.7 A case study demonstrating the descriptive capacity of a MLR model.