Sample Correlation Coefficient. We have generated N observations of correlated Nakagami-lognormal RVs following the methods described above.

In order to evaluate consistency and dependency of measured and simulated data,

sample correlation coefficient (r) is determined as follows:

r =

Sample correlation coefficient [C.sub.v](RMSE) = Coefficient of variation of the root-mean-square error n = Sample size Subscripts s = simulation variable m = measured variable i = index REFERENCES

where r is the

sample correlation coefficient between R and I.

A 2 x 2 variance-covariance matrix [V.sub.rec] is used to describe the complex-valued variable S in terms of its standard uncertainties u(R) and u(I) for R and I and the

sample correlation coefficient r between R and I in a rectangular coordinate,

Possibly these disparities resulted from variability of the

sample correlation coefficient for small N.

R([y.sub.1],[y.sub.2]) = the

sample correlation coefficient between variable [y.sub.1] and [y.sub.2];

1) Pearson's

sample correlation coefficient, let's denote it by [r.sub.p]

3.19 Distribution of the

Sample Correlation Coefficient From Bivariate Normal Distribution

The positive correlation between X and Y is measured by the

sample correlation coefficient (r).

David's tables of the

sample correlation coefficient: distribution function and percentiles.