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

Hypothesis testing


Suppose a pharmaceutical company claims that their allergy medicine is 90% effective in relieving allergies for a 12-hour period. To test that claim, an independent laboratory conducts an experiment with 200 subjects. Of them, only 160 report that the medicine was, as claimed, effective against allergies for 12 hours. The laboratory must determine whether that data is sufficient to reject the company's claim.

To set up the analysis, we first identify the population, the random sample, the relevant random variable, its distribution, and the hypothesis to be tested. In this case, the population could be all potential consumers of the medicine, the random sample is the set of n = 200 subjects reporting their results, and the random variable X is the number of those who did get the promised allergy relief. This random variable has the binomial distribution, with p being the probability that any one person does get that relief from taking the medicine. Finally, the hypothesis...