Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. 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:
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课程信息
您将获得的技能
- Statistics
- Linear Regression
- R Programming
- Linear Algebra
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约翰霍普金斯大学
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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Background
We cover some basic matrix algebra results that we will need throughout the class. This includes some basic vector derivatives. In addition, we cover some some basic uses of matrices to create summary statistics from data. This includes calculating and subtracting means from observations (centering) as well as calculating the variance.
One and two parameter regression
In this module, we cover the basics of regression through the origin and linear regression. Regression through the origin is an interesting case, as one can build up all of multivariate regression with it.
Linear regression
In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships.
General least squares
We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome.
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来自ADVANCED LINEAR MODELS FOR DATA SCIENCE 1: LEAST SQUARES的热门评论
I really enjoyed the course. It was well explained and the quizzes at regular intervals were helpful. It would be great if there were some practice exercises though...
chapter on bases showing four equivalent forms was brilliant! Hoping to learn BLUE, GAMs in part 2.
Great refresher of linear algebra
understood many things about linear models that I just knew superficially from its cores
This is an excellent course that enabled me to understand how multiple regression in linear models works behind the hood. The practical examples shown by the professor were very helpful. Thank you
关于 Advanced Statistics for Data Science 专项课程
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.

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