# Lecture 13 – Ordinary Least Squares

Presented by Suraj Rampure, Andrew Bray

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

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 | |
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13.0 Introduction. |
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13.1 A quick recap of the modeling process, and a roadmap for lecture. |
13.1 | |

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 [EXTRA] A case study demonstrating the descriptive capacity of a MLR model. |