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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 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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