Advanced Linear Models for Data Science 2
Master advanced statistical linear models with a focus on least squares analysis and multivariate regression for data science applications.
Master advanced statistical linear models with a focus on least squares analysis and multivariate regression for data science 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
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Master multivariate expected values and covariance matrices
Understand multivariate normal distribution properties
Apply distributional results in regression analysis
Develop confidence intervals and prediction intervals
Analyze residuals and PRESS statistics
Skills you'll gain
This course includes:
2.6 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course provides a comprehensive exploration of statistical linear models with a focus on least squares from a linear algebraic and mathematical perspective. Students learn about multivariate expected values, the multivariate normal distribution, distributional results in regression, and residual analysis. The curriculum emphasizes mathematical rigor and theoretical foundations while building practical understanding of regression modeling for data science applications.
Introduction and expected values
Module 1 · 1 Hours to complete
The multivariate normal distribution
Module 2 · 1 Hours to complete
Distributional results
Module 3 · 1 Hours to complete
Residuals
Module 4 · 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
95 ratings
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