Advanced Linear Models for Data Science 1: Least Squares
Master linear regression and least squares analysis through a rigorous mathematical approach with practical R programming applications.
Master linear regression and least squares analysis through a rigorous mathematical approach with practical R programming applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Advanced Statistics for Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Master matrix algebra and vector derivatives
Understand regression through origin and linear regression
Implement general least squares analysis
Create and analyze design matrices
Work with basis expansions and residuals
Develop practical R programming skills
Skills you'll gain
This course includes:
3.4 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course provides a mathematical foundation in least squares and linear regression from a linear algebraic perspective. Students learn matrix algebra, vector derivatives, and their applications to regression modeling. The curriculum covers topics from basic one-parameter regression through general least squares, including practical implementations in R programming. The course emphasizes both theoretical understanding and practical application through coding examples.
Background
Module 1 · 1 Hours to complete
One and two parameter regression
Module 2 · 1 Hours to complete
Linear regression
Module 3 · 1 Hours to complete
General least squares
Module 4 · 1 Hours to complete
Least squares examples
Module 5 · 1 Hours to complete
Bases and residuals
Module 6 · 1 Hours to complete
Fee Structure
Instructor
Distinguished Biostatistician and Neuroinformatics Expert at Johns Hopkins
Dr. Brian Caffo serves as a Professor in the Department of Biostatistics at Johns Hopkins University Bloomberg School of Public Health. After earning his PhD from the University of Florida's Department of Statistics in 2001, he has established himself as a leader in computational statistics and neuroinformatics. As co-creator of the SMART working group, he has made significant contributions to statistical methodology and brain imaging research. His exceptional achievements have been recognized with the Presidential Early Career Award for Scientists and Engineers (PECASE), as well as the Bloomberg School of Public Health's Golden Apple and AMTRA teaching awards, highlighting his excellence in both research and education.
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4.5 course rating
187 ratings
Frequently asked questions
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