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 normal distribution


The normal distribution is a theoretical distribution that idealizes many distributions like the one in Figure 3-20. It is also called the bell shape curve or the Gaussian distribution, after its discoverer, Carl Friedrich Gauss (1777-1855).

The shape of the normal distribution is the graph of the following function:

Here, m is the mean and s is the standard deviation. The symbols e and are a mathematical constant: e = 2.7182818 and = 3.14159265. This function is called the density function for the (theoretical) distribution.

Note the distinction between the four symbols , s, m, and s. The first two are computed from the actual sample values; the second two are parameters used to define a theoretical distribution.

A thought experiment

To see how the normal distribution relates to actual statistics, imagine an experiment where you have a large flat clear jar that contains n (balanced) coins. When you shake the jar, some number x of those coins will settle heads up....