Statistical inference for data science
This book is 99% complete
Last updated on 2016-05-24
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 book gives a rigorous treatment of the elementary concepts in statistical inference from a classical frequentist perspective. After reading this book and performing the exercises, the student will understand the basics of hypothesis testing, confidence intervals and probability. Check out the status of the book at github https://github.com/bcaffo/LittleInferenceBook
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The Book + Videos
This is the book plus all of the videos in MP4 format. Not necessary if you just want to watch the videos on YouTube or Coursera.
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Video lectures associated with the book.
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The book + videos + homework videos + github repos
This contains the book, the videos for the course, the homework solutions and the course repositories. Not necessary if you want to get the videos on YouTube or Coursera and the repos from github.
Includes:
Videos
Video lectures associated with the book.
Homework Videos
This is all of the homework videos downloadable. The filename contains the problem numbers. These are all available on YouTube as well.
Github repos
This contains the github repo for the Coursera Class and the homework.
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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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