Advanced Linear Models for Data Science 2: Statistical Linear Models
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Advanced Linear Models for Data Science 2: Statistical Linear Models
Johns Hopkins University
Informações sobre o curso
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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.
Programa - O que você aprenderá com este curso
1
Seção
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.
7 vídeos (Total de 38 min), 3 leituras, 1 teste
7 videos
Multivariate expected values, the basics4min
Expected values, matrix operations2min
Multivariate variances and covariances5min
Multivariate covariance and variance matrix operations5min
Expected values of quadratic forms3min
Expected value properties of least squares estimates13min
3 leituras
Welcome to the class10min
Course textbook10min
Introduction to expected values10min
1 exercício prático
Expected Values30min
2
Seção
The multivariate normal distribution
In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
4 vídeos (Total de 31 min), 2 leituras, 1 teste
4 videos
The singular normal distribution7min
Normal likelihoods5min
Normal conditional distributions8min
2 leituras
Introduction to the multivariate normal10min
A note on the last quiz question.10min
1 exercício prático
the multivariate normal20min
3
Seção
Distributional results
In this module, we build the basic distributional results that we see in multivariable regression.
8 vídeos (Total de 60 min), 1 leitura, 1 teste
8 videos
Confidence intervals for regression coefficients6min
F distribution4min
Coding example7min
Prediction intervals11min
Coding example5min
Confidence ellipsoids7min
Coding example6min
1 leituras
Distributional results10min
1 exercício prático
Distributional results20min
4
Seção
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.
4 vídeos (Total de 32 min), 2 leituras, 1 teste
4 videos
Code demonstration3min
Leave one out residuals8min
Press residuals14min
2 leituras
Residuals10min
Thanks for taking the course10min
1 exercício prático
Residuals14min
Instrutores
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Sobre 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.
Perguntas Frequentes – FAQ
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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