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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
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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

Random variables

To understand statistical data analysis, we must first understand the concept of a random variable.

A random variable is a function that assigns a number to each element of a sample space. By converting symbolic outcomes such as HTTH, they allow for simpler mathematical analysis of the data.

The coin example illustrates this. The sample space S1 has 16 outcomes. For each outcome x, let X(x) be the number of heads in that outcome. For example, X(HHTH) = 3, and X(TTTT) = 0. This is the same as the transformation of S1 into S2 in the previous discussion. Here, we are using the random variable X for the same purpose. Now we can translate statements about the probability functions p and P into statements about purely numerical functions.

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