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

Choosing a prior


Notice that the posterior distribution looks a little different depending on what prior you use. The most common criticism leveled against Bayesian methods is that the choice of prior adds an unsavory subjective element to analysis. To a certain extent, they're right about the added subjective element, but their allegation that it is unsavory is way off the mark.

To see why, check out the following figure, which shows both posterior distributions (from priors #1 and #2) in the same plot. Notice how priors #1 and #2-two very different priors-given the evidence, produce posteriors that look more similar to each other than the priors did:

Figure 7.6: The posterior distributions from prior #1 and #2

Now direct your attention to the following figure, which shows the posterior of both priors if the evidence included 80 out of 120 correct trials:

Figure 7.7: The posterior distributions from prior #1 and #2 with more evidence

Note that the evidence still contains 67% correct trials,...