#### Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
RefresheR
The Shape of Data
Describing Relationships
Probability
Using Data To Reason About The World
Testing Hypotheses
Bayesian Methods
The Bootstrap
Predicting Continuous Variables
Predicting Categorical Variables
Predicting Changes with Time
Sources of Data
Dealing with Missing Data
Dealing with Messy Data
Dealing with Large Data
Working with Popular R Packages
Reproducibility and Best Practices
Other Books You May Enjoy
Index

## Exercises

Practice the following exercises to reinforce the concepts learned in this chapter:

• Recall the drug testing at Daisy Girl, Inc. earlier in the chapter. We used .1% as our prior probability that the employee was using the drug. Why should this prior have been even lower? Using a subjective Bayesian interpretation of probability, estimate what the prior should have been given that the employee was able to hold down a job and no one saw her/him act like an alligator.
• Hark back to the example of the coin from Larry the Untrustworthy Knave. We would expect the proportion of heads in a fair coin that is flipped many times to be around 50%. In Larry's coin, the proportion was 2/3, which is unlikely to occur. The probability of 20 heads in 30 flips was 2.1%. However, find the probability of getting 40 heads in 60 flips. Even though the proportions are the same, why is the probability of observing 40 heads in 60 flips so significantly less probable? Understanding the answer to this question...