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Clojure for Data Science

You're reading from  Clojure for Data Science

Product type Book
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Pages 608 pages
Edition 1st Edition
Languages
Author (1):
Henry Garner Henry Garner
Profile icon Henry Garner

Table of Contents (18) Chapters

Clojure for Data Science
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
1. Statistics 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Pearson's correlation


Pearson's correlation is often given the variable name r and is calculated in the following way, where dxi and dyi are calculated as before:

Since the standard deviations are constant values for the variables X and Y the equation can be simplified to the following, where σx and σy are the standard deviations of X and Y respectively:

This is sometimes referred to as Pearson's product-moment correlation coefficient or simply just the correlation coefficient and is usually denoted by the letter r.

We have previously written functions to calculate the standard deviation. Combining with our function to calculate covariance yields the following implementation of Pearson's correlation:

(defn correlation [x y]
  (/ (covariance x y)
     (* (standard-deviation x)
        (standard-deviation y))))

Alternately, we can make use of the incanter.stats/correlation function.

Because standard scores are dimensionless, so is r. If r is -1.0 or 1.0, the variables are perfectly negatively or...

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