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Big Data Analysis with Python

You're reading from  Big Data Analysis with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Pages 276 pages
Edition 1st Edition
Languages
Authors (3):
Ivan Marin Ivan Marin
Profile icon Ivan Marin
Ankit Shukla Ankit Shukla
Profile icon Ankit Shukla
Sarang VK Sarang VK
Profile icon Sarang VK
View More author details

Table of Contents (11) Chapters

Big Data Analysis with Python
Preface
1. The Python Data Science Stack 2. Statistical Visualizations 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

Correlation


Correlation is a statistical measure of the level of association between two numerical variables. It gives us an idea of how closely two variables are related with each other. For example, age and income are quite closely related variables. It has been observed that the average income grows with age within a threshold. Thus, we can assume that age and income are positively correlated with each other.

Note

However, correlation does not establish a cause-effect relationship. A cause-effect relationship means that one variable is causing a change in another variable.

The most common metric used to compute this association is the Pearson Product-Moment Correlation, commonly known as Pearson correlation coefficient or simply as the correlation coefficient. It is named after its inventor, Karl Pearson.

The Pearson correlation coefficient is computed by dividing the covariance of the two variables by the product of their standard deviations. The correlation value lies between -1 and +1...

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