Linear Regression for Data Science: Implementation with R
Master linear regression techniques for data analysis using R, with practical applications in statistical modeling and confounding adjustment.
Master linear regression techniques for data analysis using R, with practical applications in statistical modeling and confounding adjustment.
This course provides a comprehensive introduction to linear regression in data science, focusing on implementing regression analysis using R programming. Students learn to quantify relationships between variables and adjust for confounding effects through practical examples, including a case study on baseball analytics inspired by Moneyball. The course covers the historical development of linear regression by Galton, techniques for detecting and managing confounding variables, and practical implementation strategies in R for data analysis.
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What you'll learn
Understand the historical development of linear regression through Galton's work
Master the identification and management of confounding variables
Implement linear regression analysis using R programming
Apply statistical modeling techniques to real-world datasets
Interpret regression results and assess model validity
Develop practical data analysis skills for research and business applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course offers a practical approach to understanding and implementing linear regression in data science applications. The curriculum covers fundamental concepts of linear regression, from its historical development to modern applications in data analysis. Students learn to identify and handle confounding variables, implement regression analysis in R, and interpret results effectively. Using real-world examples like baseball analytics, the course demonstrates how to apply linear regression techniques to solve practical problems and make data-driven decisions.
Fee Structure
Instructor
32 Courses
Harvard Biostatistics Professor and Genomics Data Analysis Pioneer
Rafael Irizarry is a distinguished Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. His expertise spans genomics, data analysis, and the R programming language. Irizarry's career has been marked by significant contributions to the field of genomics data analysis over the past two decades
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4.1 course rating
25 ratings
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