Book Image

Data Analysis with R

Book Image

Data Analysis with R

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. With over 7,000 user contributed packages, it’s easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R 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. 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 (20 chapters)
Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Simple linear regression with a binary predictor


One of the coolest things about linear regression is that we are not limited to using predictor variables that are continuous. For example, in the last section, we used the continuous variable wt (weight) to predict miles per gallon. But linear models are adaptable to using categorical variables, like am (automatic or manual transmission) as well.

Normally, in the simple linear regression equation , will hold the actual value of the predictor variable. In the case of a simple linear regression with a binary predictor (like am), will hold a dummy variable instead. Specifically, when the predictor is automatic, will be 0, and when the predictor is manual, will be 1.

More formally:

Put in this manner, the interpretation of the coefficients changes slightly, since the will be zero when the car is automatic, is the mean miles per gallon for automatic cars.

Similarly, since will equal when the car is manual, is equal to the mean difference...