Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

The independence of probabilistic events


We say that the two events E and F are independent if P(F|E) = P(F). In other words, the occurrence of E has no effect upon the probability of F. From the previous formula, we can see that this definition is equivalent to the condition:

This shows that the definition is symmetric: E is independent of F if and only if F is independent of E.

In our preceding marble example, E = (1st is R) and let F = (2nd is G). Since P(F|E) = 80% and P(F) = 67%, we see that E and F are not independent. Obviously, F depends on E.

For another example, consider the previous Motor Vehicle example. Let E = (driver owns 2 vehicles) and F = (driver drives at least 10,000 miles/year). We can compute the unconditional probabilities from the marginal data: P(E) = 0.31 and P(F) = 0.36 + 0.19 = 0.55. So P(E)P(F) = (0.31)(0.55) = 0.17. But P(E ∩ F)= 0.12 + 0.08 = 0.20 ≠ 0.17, so these two events are not independent. Whether a person drives a lot depends upon the number of vehicles...