What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Advanced Statistics for Data Science Specialization
Instructor: Brian Caffo, PhD
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Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. 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.
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.
7 videos3 readings1 assignment
In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
4 videos2 readings1 assignment
In this module, we build the basic distributional results that we see in multivariable regression.
8 videos1 reading1 assignment
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.
4 videos2 readings1 assignment
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Reviewed on Jan 13, 2023
Great !!! Learning time and I enjoy the math side of it...
Reviewed on Apr 2, 2020
This is a great course from Johns Hopkins University . By taking this course, I improved my Data Management, Statistical Programming, and Statistics skills.
Reviewed on Oct 12, 2019
It is a very good course for any statistics to learn and have a sweet tastes of math and its behind functionality on data.
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