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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

Performing the t-test


The difference in the way t-test works stems from the probability distribution from which our p-value is calculated. Having calculated our t-statistic, we need to look up the value in the t-distribution parameterized by the degrees of freedom of our data:

(defn t-test [a b]
  (let [df (+ (count a) (count b) -2)]
    (- 1 (s/cdf-t (i/abs (t-stat a b)) :df df))))

The degrees of freedom are two less than the sizes of the samples combined, which is 298 for our samples.

Recall that we are performing a hypothesis test. So, let's state our null and alternate hypotheses:

  • H0: This sample is drawn from a population with a supplied mean

  • H1: This sample is drawn from a population with a greater mean

Let's run the example:

(defn ex-2-16 []
  (let [data (->> (load-data "new-site.tsv")
                  (:rows)
                  (group-by :site)
                  (map-vals (partial map :dwell-time)))
        a (get data 0)
        b (get data 1)]
    (t-test a b)))

;; 0.0503

This...

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