If correlation estimates were normally distributed, estimates of confidence intervals would be straightforward. However, correlation estimates are bound within the range of –1.0 to +1.0. The common technique devised by R.A. Fisher involves a nonlinear transformation of correlation functions into random variables that are approximately normal. The technique is known as Fisher’s z-transformation and is formally expressed as ρ = tanh(z), where tanh( ) is the hyperbolic tangent function.
More simply, the transformed standard error, z, is defined as z = (1/2) ln[ (1 + ρ) / (1 – ρ) ]. The variance is a function of the number of paired samples in the sets being correlated and is defined as var(z) = 1 / (n – 8/3).
Thus, for example, the 95% confidence interval around a correlation estimate would be from (z – 1.96 σ) to (z + 1.96 σ) where σ = sqrt( 1/ (n – 8/3) ).
The iPad version allows you to add, retrieve, reorder, or delete data sets. Also, you may email, send via texting, and print data and results.
– Various code changes.