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:
Offered By
About this Course
Offered by

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.
Syllabus - What you will learn from this course
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.
The multivariate normal distribution
In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
Distributional results
In this module, we build the basic distributional results that we see in multivariable regression.
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.
Reviews
TOP REVIEWS FROM ADVANCED LINEAR MODELS FOR DATA SCIENCE 2: STATISTICAL LINEAR MODELS
This is a great course from Johns Hopkins University . By taking this course, I improved my Data Management, Statistical Programming, and Statistics skills.
This course is very powerfull for statistical linear
Good course on applied linear statistical modeling.
About the Advanced Statistics for Data Science Specialization
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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