À propos de ce cours
4.7
25 ratings
2 reviews
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....
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Advanced Level

Niveau avancé

Clock

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

Approx. 9 heures pour terminer
Comment Dots

English

Sous-titres : English
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Advanced Level

Niveau avancé

Clock

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

Approx. 9 heures pour terminer
Comment Dots

English

Sous-titres : English

Programme du cours : ce que vous apprendrez dans ce cours

1

Section
Clock
2 heures pour terminer

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 vidéos (Total 38 min), 3 lectures, 1 quiz
Video7 vidéos
Multivariate expected values, the basics4 min
Expected values, matrix operations2 min
Multivariate variances and covariances5 min
Multivariate covariance and variance matrix operations5 min
Expected values of quadratic forms3 min
Expected value properties of least squares estimates13 min
Reading3 lectures
Welcome to the class10 min
Course textbook10 min
Introduction to expected values10 min
Quiz1 exercice pour s'entraîner
Expected Values30 min

2

Section
Clock
1 heure pour terminer

The multivariate normal distribution

In this module, we build up the multivariate and singular normal distribution by starting with iid normals....
Reading
4 vidéos (Total 31 min), 2 lectures, 1 quiz
Video4 vidéos
The singular normal distribution7 min
Normal likelihoods5 min
Normal conditional distributions8 min
Reading2 lectures
Introduction to the multivariate normal10 min
A note on the last quiz question.10 min
Quiz1 exercice pour s'entraîner
the multivariate normal20 min

3

Section
Clock
1 heure pour terminer

Distributional results

In this module, we build the basic distributional results that we see in multivariable regression....
Reading
8 vidéos (Total 60 min), 1 lecture, 1 quiz
Video8 vidéos
Confidence intervals for regression coefficients6 min
F distribution4 min
Coding example7 min
Prediction intervals11 min
Coding example5 min
Confidence ellipsoids7 min
Coding example6 min
Reading1 lecture
Distributional results10 min
Quiz1 exercice pour s'entraîner
Distributional results20 min

4

Section
Clock
1 heure pour terminer

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 vidéos (Total 32 min), 2 lectures, 1 quiz
Video4 vidéos
Code demonstration3 min
Leave one out residuals8 min
Press residuals14 min
Reading2 lectures
Residuals10 min
Thanks for taking the course10 min
Quiz1 exercice pour s'entraîner
Residuals14 min
4.7

Meilleurs avis

par MLJan 31st 2017

Good course on applied linear statistical modeling.

Enseignant

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

À propos de 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....

Foire Aux Questions

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