Version:

Last Modified: January 12, 2018

Tests hypotheses about the association between two populations *x* and *y*.

Sampled data from population *x*.

Sampled data from population *y*.

Type of correlation test to perform.

Name | Description |
---|---|

Pearson | Tests the Pearson correlation coefficient. |

Spearman | Tests the Spearman's rank correlation coefficient. |

Kendall | Tests the Kendall rank correlation coefficient. |

**Default: **Pearson

Probability that this node incorrectly rejects a true null hypothesis.

**Default: **0.05

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an **error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Default: **No error

Hypothesis to accept if this node rejects the null hypothesis that populations *x* and *y* are uncorrelated.

Name | Value | Description |
---|---|---|

r != 0 | 0 | The correlation coefficient between x and y is not equal to 0. |

r > 0 | 1 | The correlation coefficient between x and y is greater than 0. |

r < 0 | -1 | The correlation coefficient between x and y is less than 0. |

**Default: **r != 0

A Boolean that indicates whether this node rejects the null hypothesis.

True | p value is less than or equal to significance level. This node rejects the null hypothesis and accepts the alternative hypothesis. |

False | p value is greater than significance level. This node accepts the null hypothesis and rejects the alternative hypothesis. |

Smallest significance level that leads to rejection of the null hypothesis based on the sample sets.

Lower and upper limits for the correlation coefficient of the two populations. **confidence interval** indicates the uncertainty in the estimate of the true correlation coefficient.

Lower limit of the estimate of the correlation coefficient of the two populations.

Upper limit of the estimate of the correlation coefficient of the two populations.

Sample statistics of the correlation test.

Linear correlation coefficient between *x* and *y*.

*r* critical value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **r critical value**

Let *X* represent a random variable that follows the distribution of the test statistics. **r critical value** satisfies the following equations based on the value of **alternative hypothesis**.

alternative hypothesis |
r critical value |
---|---|

r != 0 | Prob{X > r critical value} = significance level / 2 |

r > 0 | Prob{X > r critical value} = significance level |

r < 0 | Prob{X > r critical value} = significance level |

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

**error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application