Regression Models for Data Science in R
This book is 90% complete
Last updated on 2015-08-05
About the Book
The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in https://leanpub.com/LittleInferenceBook/. The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.
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The Book
This is just the boook.
English
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The Book+Videos+Code
This is the book, plus the videos, plus the video solutions. All of the videos are available on YouTube as well. The book plus lecture note github repos are included as well.
Includes:
Video lectures
These are the video lectures associated with the book. They are also available on YouTube and Coursera.
Lecture notes and code
This is the github repo zipped up as one entity. You can get this off of github if you'd like. It also includes the book repo.
English
PDF
EPUB
MOBI
APP
The Book+Code+Lecture Videos+Solution Videos
This is the book, the github repos (lecture notes and book) plus the video lectures plus the video HW solutions. All are available elsewhere for free (github and YouTube).
Includes:
Video lectures
These are the video lectures associated with the book. They are also available on YouTube and Coursera.
Lecture notes and code
This is the github repo zipped up as one entity. You can get this off of github if you'd like. It also includes the book repo.
Video HW solutions.
This is the video homework solutions. These are also all available on YouTube.
English
PDF
EPUB
MOBI
APP
About the Author
Brian Caffo is a professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. He coleads a working group, www.smart-stats.org, that focuses on the statistical analysis of imaging and biosignals. He is the recipient of the Presidential Early Career Award for Scientists and Engineers and was named a fellow of the American Statistical Association.
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