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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (20) Chapters Close

Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java Java Tools Index

The exponential distribution


Of all the probability distributions, the normal (Gaussian) distribution is maybe be the most important, because it applies to so many common phenomena. The second most important is probably the exponential distribution. Its density function is as follows:

Here, λ is a positive constant whose reciprocal is the mean (µ = 1). This distribution models the time elapsed between randomly occurring events, such as radioactive particle emission or cars arriving at a toll booth. The corresponding cumulative distribution function (CDF) is as follows:

As an example, suppose that a university help desk gets 120 calls per eight-hour day, on average. That's 15 calls per hour, or one every four minutes. We can use the exponential distribution to model this phenomenon, with mean waiting time µ = 4. That makes the density parameter λ = 1/ µ = 0.25, so:

This means, for example, that the probability that a call comes in within the next five minutes would be:

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