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The power of FME is being able to take data from multiple sources and manipulate it efficiently. So why not use FME for data science?
We’ve recently added a series of transformers to the FME hub that performs a few basic statistical tests using the RCaller or the PythonCaller.
If you don't see the statistical test you are looking for in this list, you can create your own and upload it to theFME Hubto share with other users or create a newIdeaand if it gets enough votes will add it to the list.
Learn how to create a custom transformer using either R or Python to perform the Shapiro-Wilks test (to test for the normality of a distribution). This workflow can be adapted for any statistical test using R or Python.
列出的每个变压器的FME中心页面的链接,以及一个测试工作区下载。由于R的外部软件要求,这些测试工作区不能被上传到所述轮毂。每个R-变压器的需要Rto be installed on the users' machine as well as thesqldf R package. For the Python transformers, theSciPy Python packageneeds to be installed.
Acorrelationis a test between two variables to determine their association.
Uses R to calculate if there is an association between two variables.
RCorrelation-TestWorkspace.fmwt
ACluster Analysisis a method for determining groups of data.
Uses R to calculate similar groups of data using one of three algorithms.This transformer only works for 2018.0+
RClusterCalculator-TestWorkspace.fmwt
TheShapiro-Wilks testcalculates whether a random sample of data comes from a normal distribution.
Using R and the RCaller this transformer calculates whether a random sample of data comes from a normal distribution using the Shapiro-Wilks test.
RShapiroWilks-TestWorkspace.fmwt
使用SciPy的和PythonCaller,该变压器计算数据的随机样本是否来自使用夏皮罗 - 威尔克斯测试正态分布。
PyShapiroWilks-TestWorkspace.fmwt
AT-Testis a statistical test to test if the means of two samples are significantly different from random.
The one-sample t-test tests the null hypothesis that the population mean is equal to a specified value, In other words, it tells you if the mean of your sample is close enough to a certain number to be statistically significant. This test outputs the t-value, p-value, confidence interval and the estimate.
ROneSampleTTest-TestWorkspace.fmwt
The two-sample t-test tests the mean of two groups to determine if they are significantly different or it is by random chance. This test outputs the t-value, p-value, confidence interval and the estimate.
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