Об этом курсе: 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.
Для кого этот курс: This class is for students who already have had a class in regression modeling and are familiar with the area who would like to see a more advanced treatment of the topic.
Автор: Johns Hopkins University
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Уровень | Продвинутые функции |
| Выполнение | 6 weeks of study, 1-2 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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 видео, 3 материала для самостоятельного изучения
- Reading: Welcome to the class
- Reading: Course textbook
- Reading: Introduction to expected values
- Видео: Multivariate expected values, the basics
- Видео: Expected values, matrix operations
- Видео: Multivariate variances and covariances
- Видео: Multivariate covariance and variance matrix operations
- Видео: Expected values of quadratic forms
- Видео: Expected value properties of least squares estimates
Оцениваемый: Expected Values
НЕДЕЛЯ 2
The multivariate normal distribution
In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
4 видео, 2 материала для самостоятельного изучения
- Reading: Introduction to the multivariate normal
- Видео: The singular normal distribution
- Видео: Normal likelihoods
- Видео: Normal conditional distributions
- Reading: A note on the last quiz question.
Оцениваемый: the multivariate normal
НЕДЕЛЯ 3
Distributional results
In this module, we build the basic distributional results that we see in multivariable regression.
8 видео, 1 материал для самостоятельного изучения
- Reading: Distributional results
- Видео: Confidence intervals for regression coefficients
- Видео: F distribution
- Видео: Coding example
- Видео: Prediction intervals
- Видео: Coding example
- Видео: Confidence ellipsoids
- Видео: Coding example
Оцениваемый: Distributional results
НЕДЕЛЯ 4
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 видео, 2 материала для самостоятельного изучения
- Reading: Residuals
- Видео: Code demonstration
- Видео: Leave one out residuals
- Видео: Press residuals
- Reading: Thanks for taking the course
Оцениваемый: Residuals
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Авторы
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.
Рейтинги и отзывы
Good course on applied linear statistical modeling.
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Very good... Thanks