À propos de ce cours
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.
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Advanced Level

Niveau avancé

Clock

Approx. 9 heures pour terminer

Recommandé : 6 weeks of study, 1-2 hours/week
Comment Dots

English

Sous-titres : English
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Advanced Level

Niveau avancé

Clock

Approx. 9 heures pour terminer

Recommandé : 6 weeks of study, 1-2 hours/week
Comment Dots

English

Sous-titres : English

Syllabus - What you will learn from this course

1

Section
Clock
2 hours to complete

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. ...
Reading
7 videos (Total 38 min), 3 readings, 1 quiz
Video7 videos
Multivariate expected values, the basics4m
Expected values, matrix operations2m
Multivariate variances and covariances5m
Multivariate covariance and variance matrix operations5m
Expected values of quadratic forms3m
Expected value properties of least squares estimates13m
Reading3 readings
Welcome to the class10m
Course textbook10m
Introduction to expected values10m
Quiz1 practice exercises
Expected Values30m

2

Section
Clock
1 hour to complete

The multivariate normal distribution

In this module, we build up the multivariate and singular normal distribution by starting with iid normals....
Reading
4 videos (Total 31 min), 2 readings, 1 quiz
Video4 videos
The singular normal distribution7m
Normal likelihoods5m
Normal conditional distributions8m
Reading2 readings
Introduction to the multivariate normal10m
A note on the last quiz question.10m
Quiz1 practice exercises
the multivariate normal20m

3

Section
Clock
1 hour to complete

Distributional results

In this module, we build the basic distributional results that we see in multivariable regression....
Reading
8 videos (Total 60 min), 1 reading, 1 quiz
Video8 videos
Confidence intervals for regression coefficients6m
F distribution4m
Coding example7m
Prediction intervals11m
Coding example5m
Confidence ellipsoids7m
Coding example6m
Reading1 readings
Distributional results10m
Quiz1 practice exercises
Distributional results20m

4

Section
Clock
1 hour to complete

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....
Reading
4 videos (Total 32 min), 2 readings, 1 quiz
Video4 videos
Code demonstration3m
Leave one out residuals8m
Press residuals14m
Reading2 readings
Residuals10m
Thanks for taking the course10m
Quiz1 practice exercises
Residuals14m
4.7

Top Reviews

By MLJan 31st 2017

Good course on applied linear statistical modeling.

Instructor

Avatar

Brian Caffo, PhD

Professor, Biostatistics

About Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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