#### 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

## Interval estimation

Again, we care about the standard error (the standard deviation of the sampling distribution of sample means) because it expresses the degree of uncertainty we have in our estimation. Due to this, it's not uncommon for statisticians to report the standard error along with their estimate.

What's more common, though, is for statisticians to report a range of numbers to describe their estimates; this is called interval estimation. In contrast, when we were just providing the sample mean as our estimate of the population mean, we were engaging in point estimation.

One common approach to interval estimation is to use confidence intervals. A confidence interval gives us a range over which a significant proportion of the sample means would fall when samples are repeatedly drawn from a population and their means are calculated. Concretely, a 95 percent confidence interval is the range that would contain 95 percent of the sample means if multiple samples were taken from the same...