-
- About this book
- About the picture on the cover
-
1. Introduction
- Before beginning
- Statistical inference defined
- Summary notes
- The goals of inference
- The tools of the trade
- Different thinking about probability leads to different styles of inference
- Exercises
-
2. Probability
- Where to get a more thorough treatment of probability
- Kolmogorov’s Three Rules
- Consequences of The Three Rules
- Random variables
- Probability mass functions
- Probability density functions
- CDF and survival function
- Quantiles
- Exercises
-
3. Conditional probability
- Conditional probability, motivation
- Conditional probability, definition
- Bayes’ rule
- Diagnostic Likelihood Ratios
- Independence
- IID random variables
- Exercises
-
4. Expected values
- The population mean for discrete random variables
- The sample mean
- Continuous random variables
- Simulation experiments
- Summary notes
- Exercises
-
5. Variation
- The variance
- The sample variance
- Simulation experiments
- The standard error of the mean
- Data example
- Summary notes
- Exercises
-
6. Some common distributions
- The Bernoulli distribution
- Binomial trials
- The normal distribution
- The Poisson distribution
- Exercises
-
7. Asymptopia
- Asymptotics
- Limits of random variables
- The Central Limit Theorem
- CLT simulation experiments
- Confidence intervals
- Simulation of confidence intervals
- Poisson interval
- Summary notes
- Exercises
-
8. t Confidence intervals
- Small sample confidence intervals
- Gosset’s t distribution
- The data
- Independent group t confidence intervals
- Confidence interval
- Mistakenly treating the sleep data as grouped
- Unequal variances
- Summary notes
- Exercises
-
9. Hypothesis testing
- Hypothesis testing
- Types of errors in hypothesis testing
- Discussion relative to court cases
- Building up a standard of evidence
- General rules
- Two sided tests
- T test in R
- Connections with confidence intervals
- Two group intervals
- Exact binomial test
- Exercises
-
10. P-values
- Introduction to P-values
- What is a P-value?
- The attained significance level
- Binomial P-value example
- Poisson example
- Exercises
-
11. Power
- Power
- Question
- Notes
- T-test power
- Exercises
-
12. The bootstrap and resampling
- The bootstrap
- The bootstrap principle
- Group comparisons via permutation tests
- Permutation tests
- Variations on permutation testing
- Permutation test B v C
- Exercises
Statistical inference for data science
A companion to the Coursera Statistical Inference Course
This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.
This book is available in multiple packages!
This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.
PDF
EPUB
WEB
78,491
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124
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About
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
Packages
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All packages include the ebook in the following formats: PDF, EPUB, and Web
The Book
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Suggested price$14.99Free!
The Book + Videos
Minimum price
Suggested price$19.99This 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.
$14.99
- VideosVideo lectures associated with the book.
This book is also available in the following packages:
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.
- VideosVideo lectures associated with the book.
- Homework VideosThis is all of the homework videos downloadable. The filename contains the problem numbers. These are all available on YouTube as well.
- Github reposThis contains the github repo for the Coursera Class and the homework.
- Minimum price
- $19.99
- Suggested price
- $24.99
- Videos
Author
About the Author
Brian Caffo
Brian Caffo, PhD is a professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Along with Roger Peng and Jeff Leek, Dr. Caffo created the Data Science Specialization on Coursera. Dr. Caffo is leading expert in statistics and biostatistics and is the recipient of the PECASE award, the highest honor given by the US Government for early career scientists and engineers.
Episode 21
An Interview with Brian Caffo
Contents
Table of Contents
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