Monthly Subscription Starting at AED 99 + VAT

Advanced Linear Models for Data Science 2: Statistical Linear Models
  • Level Expert
  • Duration 6 hours
  • Course by Johns Hopkins University
  • Offered by

About

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.

Modules

Welcome!

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Introductory video

2 Readings

  • Welcome to the class
  • Course textbook

Expected values

    • 6 Videos
    • 1 Readings
Show info about module content

6 Videos

  • 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

1 Readings

  • Introduction to expected values

Quiz 1: Expected Values

    • 1 Assignment
Show info about module content

1 Assignment

  • Expected Values

Multivariate normal

    • 4 Videos
    • 1 Readings
Show info about module content

4 Videos

  • Normals and multivariate normals
  • The singular normal distribution
  • Normal likelihoods
  • Normal conditional distributions

1 Readings

  • Introduction to the multivariate normal

Quiz 2

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • the multivariate normal

1 Readings

  • A note on the last quiz question.

Introduction

    • 1 Readings
Show info about module content

1 Readings

  • Distributional results

Distributional results

    • 8 Videos
Show info about module content

8 Videos

  • Chi squared results for quadratic forms
  • Confidence intervals for regression coefficients
  • F distribution
  • Coding example
  • Prediction intervals
  • Coding example
  • Confidence ellipsoids
  • Coding example

Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Distributional results

Introduction

    • 1 Readings
Show info about module content

1 Readings

  • Residuals

Residuals

    • 4 Videos
Show info about module content

4 Videos

  • Residuals distributional results
  • Code demonstration
  • Leave one out residuals
  • Press residuals

Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Residuals

Thanks

    • 1 Readings
Show info about module content

1 Readings

  • Thanks for taking the course

Auto Summary

Advance your expertise with "Advanced Linear Models for Data Science 2: Statistical Linear Models" on Coursera. Focused on maths and statistics, this expert-level course delves into least squares from a linear algebraic perspective. Ideal for those with a background in linear algebra, calculus, statistics, and R programming, it strengthens your foundation in regression modeling. With a duration of 360 minutes, it offers flexible subscription options: Starter and Professional. Perfect for applied data scientists seeking deeper insights.

Brian Caffo, PhD
Instructor

Brian Caffo, PhD