Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

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.

Course 4 of 4 in the Advanced Statistics for Data Science Specialization

Syllabus

WEEK 1

Introduction and expected values

In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.

WEEK 2

The multivariate normal distribution

In this module, we build up the multivariate and singular normal distribution by starting with iid normals.

WEEK 3

Distributional results

In this module, we build the basic distributional results that we see in multivariable regression.

WEEK 4

Residuals

In this module we will revisit residuals and consider their distributional results. We also consider the so-called PRESS residuals and show how they can be calculated without re-fitting the model.