Johns Hopkins University - Advanced Linear Models for Data Science 2: Statistical Linear Models
- Offered byCoursera
- Public/Government Institute
Advanced Linear Models for Data Science 2: Statistical Linear Models at Coursera Overview
Duration | 6 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Advanced Linear Models for Data Science 2: Statistical Linear Models at Coursera Highlights
- Earn a certificate from the university of Johns Hopkins upon completion of course.
- Flexible deadlines according to your schedule.
Advanced Linear Models for Data Science 2: Statistical Linear Models at Coursera Course details
- 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.
Advanced Linear Models for Data Science 2: Statistical Linear Models at Coursera Curriculum
Introduction and expected values
Introductory video
Multivariate expected values, the basics
Expected values, matrix operations
Multivariate variances and covariances
Multivariate covariance and variance matrix operations
Expected values of quadratic forms
Expected value properties of least squares estimates
Welcome to the class
Course textbook
Introduction to expected values
Expected Values
The multivariate normal distribution
Normals and multivariate normals
The singular normal distribution
Normal likelihoods
Normal conditional distributions
Introduction to the multivariate normal
A note on the last quiz question.
the multivariate normal
Distributional results
Chi squared results for quadratic forms
Confidence intervals for regression coefficients
F distribution
Coding example
Prediction intervals
Coding example
Confidence ellipsoids
Coding example
Distributional results
Distributional results
Residuals
Residuals distributional results
Code demonstration
Leave one out residuals
Press residuals
Residuals
Thanks for taking the course
Residuals
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?