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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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Toc

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

One-sample t-test


Independent samples of t-tests are the most common sort of statistical analysis, which provide a very flexible and generic way of comparing whether two samples represent the same or different population. However, in cases where the population mean is already known, there is an even simpler test represented by s/simple-t-test.

We pass a sample and a population mean to test against with the :mu keyword. So, if we simply want to see whether our new site is significantly different from the previous population mean dwell time of 90s, we can run a test like this:

(defn ex-2-18 []
  (let [data (->> (load-data "new-site.tsv")
                  (:rows)
                  (group-by :site)
                  (map-vals (partial map :dwell-time)))
        b (get data 1)]
    (clojure.pprint/pprint (s/t-test b :mu 90))))

;; {:p-value 0.13789520958229406,
;;  :df 15,
;;  :n2 nil,
;;  :x-mean 122.0,
;;  :y-mean nil,
;;  :x-var 6669.866666666667,
;;  :conf-int [78.48152745280898 165...
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