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

Summary


Whew, we've been through a lot in this chapter, and I commend you for sticking it out. Your tenacity will be well rewarded when you start using regression analysis in your own projects or to research like a professional.

We started off with the basics: how to describe a line, simple linear relationships, and how a best-fit regression line is determined. You saw how we can use R to easily plot these best-fit lines.

We went on to explore regression analysis with more than one predictor. You learned how to interpret the loquacious lm summary output, and what everything meant. In the context of multiple regression, you learned how the coefficients are properly interpreted as the effect of a predictor controlling for all other predictors. You're now aware that controlling for and thinking about confounds is one of the cornerstones of statistical thinking.

We discovered that we weren't limited to using continuous predictors, and that, using dummy coding, we can not only model the effects...