# Lecture 14 – Ordinary Least Squares

Presented by Anthony D. Joseph and Suraj Rampure

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

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 | |
---|---|---|

14.1 A quick recap of the modeling process, and a roadmap for lecture. |
14.1 | |

14.2 Defining the multiple linear regression model using linear algebra (dot products and matrix multiplication). Introducing the idea of a design matrix. |
14.2 | |

14.3 Defining the mean squared error of the multiple linear regression model as the (scaled) norm of the residual vector. |
14.3 | |

14.4 Using a geometric argument to determine the optimal model parameter. |
14.4 | |

14.5 Residual plots. Properties of residuals, with and without an intercept term in our model. |
14.5 | |

14.6 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. |
14.6 |