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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 (14) Chapters Close

Preface 1. Introduction to Data Analysis 2. Data Preprocessing FREE CHAPTER 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 A. Java Tools Index

The standard normal distribution


Recall from Chapter 3, Data Visualization, that the normal distribution's probability density function is:

where μ is the population mean and σ is the population standard deviation. Its graph is the well-known bell curve, centered at where x = μ and roughly covering the interval from x = μ–3σ to x = μ+3σ (that is, x = μ±3σ). In theory, the curve is asymptotic to the x axis, never quite touching it, but getting closer as x approaches ±∞.

If a population is normally distributed, then we would expect over 99% of the data points to be within the μ±3σ interval. For example, the American College Board Scholastic Aptitude Test in mathematics (AP math test) was originally set up to have a mean score of μ = 500 and a standard deviation of σ = 100. This would mean that nearly all the scores would fall between μ+3σ = 800 and μ–3σ = 200.

When μ = 0 and σ = 1, we have a special case called the standard normal distribution.

Figure 4-12. The standard normal distribution

Its...

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