Tests hypotheses about the association between two variables x and y.


icon

Inputs/Outputs

  • c1ddbl.png sample set x

    sample set x contains the sampled data from the first variable x.

  • c1ddbl.png sample set y

    sample set y contains the sampled data from the second variable y.

  • cenum.png type

    type specifies the type of correlation test to perform.

    0
    Pearson
    1
    Spearman
    2
    Kendall
  • cdbl.png significance level

    significance level specifies the probability that the hypothesis test conclusion is wrong based on the sample set x and sample set y.

  • ci32.png alternative hypothesis

    alternative hypothesis specifies the hypothesis to accept if LabVIEW rejects the null hypothesis that the variables x and y are uncorrelated.

    –1r > 0—The correlation coefficient r is greater than 0.
    0r != 0—The correlation coefficient r is not equal to 0.
    1r < 0—The correlation coefficient r is less than 0.
  • ibool.png reject null hypothesis

    reject null hypothesis indicates whether to reject the null hypothesis, with significance level being the probability of reaching the wrong conclusion.

    If p value is less than or equal to significance level, reject null hypothesis returns TRUE. Reject the null hypothesis and accept the alternative hypothesis. If p value is greater than significance level, reject null hypothesis returns FALSE. Accept the null hypothesis and reject the alternative hypothesis.

  • idbl.png p value

    p value returns the probability that you incorrectly rejected the null hypothesis.

  • idbl.png rsig

    rsig returns the minimum value of the correlation coefficient for which you should reject the null hypothesis.

  • ii32.png error

    error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

  • idbl.png correlation coefficient r

    correlation coefficient r returns the linear correlation coefficient between x and y.