Advanced Linear Models for Data Science 1: Least Squares

61 ratings

Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares

61 ratings

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.
After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

From the lesson

General least squares

We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome.