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 binomial distribution


The binomial distribution is defined by this formula for its PDF:

, for x = 0, 1, …, n

Here, n and p are parameters: n must be a positive integer and 0 ≤ p ≤ 1. The symbol is called a binomial coefficient. It can be computed from the following formula:

The exclamation point (!) stands for factorial, which means to multiply the integer by all its preceding positive integers. For example, five factorial is 5! = 5·4·3·2·1 = 120.

We encountered the binomial distribution in Chapter 3, Data Visualization, with the coin-flipping example. Here's a similar example. Suppose you have a bottle with five identical, balanced, tetrahedral dice. Each die has one face painted red and the other three faces painted green, as shown in the following figure:

Figure 4-7. Tetrahedral Die

The experiment is to shake the flat-bottomed bottle and observe how the five dice land. Let X be the number of dice that land with a red face down. This random variable has a binomial distribution with n =...