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Applied Unsupervised Learning with R

You're reading from   Applied Unsupervised Learning with R Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789956399
Length 320 pages
Edition 1st Edition
Languages
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Authors (2):
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Bradford Tuckfield Bradford Tuckfield
Author Profile Icon Bradford Tuckfield
Bradford Tuckfield
Alok Malik Alok Malik
Author Profile Icon Alok Malik
Alok Malik
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Toc

Introduction to the Kolmogorov-Smirnov Test


Now that we’ve learned how to generate the probability density functions of datasets that don't closely resemble standard distributions, we’ll learn how to perform some tests to distinguish these nonstandard distributions from each other.

Sometimes, we're given multiple observed samples of data and we want to find out whether those samples belong to the same distribution or not. In the case of standard distributions, we have multiple tests, such as Student's t-test and z-test, to determine this. For non-standard distributions, or where we don't know the type of distribution, we use the Kolmogorov-Smirnov test. To understand the Kolmogorov-Smirnov test, you first need to understand a few terms:

  • Cumulative Distribution Function (CDF): This is a function whose value gives the probability of a random variable being less than or equal to the argument of the function.

  • Null Hypothesis: In hypothesis testing, a null hypothesis means there is no significant...

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