Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

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.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Linear models


A small baking outfit in upstate New York called No Scone Unturned keeps careful records of the baked goods it produces. The left panel of Figure 9.1 is a scatterplot of diameters and circumferences (in centimeters) of No Scone Unturned's cookies, and depicts their relationship:

Figure 9.1: A scatterplot of diameters and circumferences of No Scone Unturned's cookies (left); the same plot with a best fit regression line plotted over the data points (right)

A straight line is the perfect thing to represent this data. After fitting a straight line to the data, we can make predictions about the circumferences of cookies that we haven't observed, such as 11 or 0.7 (if you weren't playing truant in grade school, you'd know there's a consistent and predictable relationship between the diameter of a circle and the circle's circumference, namely π, but we'll ignore that for now).

You may have learned that the equation that describes a line in a Cartesian plane is:

b is the y-intercept ...