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Advanced Linear Models for Data Science 2: Statistical Linear Models

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Advanced Linear Models for Data Science 2: Statistical Linear Models

This course is part of Advanced Statistics for Data Science Specialization

Instructor: Brian Caffo, PhD

25,018 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

108 reviews

Advanced level

Designed for those already in the industry

6 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

108 reviews

Advanced level

Designed for those already in the industry

6 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

91%

of learners achieved a positive career outcome

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This course is part of the Advanced Statistics for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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.

Module details

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.

What's included

7 videos3 readings1 assignment

7 videosTotal 38 minutes

Introductory video2 minutes
Multivariate expected values, the basics5 minutes
Expected values, matrix operations3 minutes
Multivariate variances and covariances6 minutes
Multivariate covariance and variance matrix operations6 minutes
Expected values of quadratic forms4 minutes
Expected value properties of least squares estimates14 minutes

3 readingsTotal 30 minutes

Welcome to the class10 minutes
Course textbook10 minutes
Introduction to expected values10 minutes

1 assignmentTotal 30 minutes

Expected Values30 minutes

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

What's included

4 videos2 readings1 assignment

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

What's included

8 videos1 reading1 assignment

8 videosTotal 60 minutes

Chi squared results for quadratic forms11 minutes
Confidence intervals for regression coefficients7 minutes
F distribution5 minutes
Coding example8 minutes
Prediction intervals11 minutes
Coding example5 minutes
Confidence ellipsoids7 minutes
Coding example6 minutes

1 readingTotal 10 minutes

Distributional results10 minutes

1 assignmentTotal 20 minutes

Distributional results20 minutes

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.