CALC.fisher(x)
CALC.binomdist(number_s, trials, probability_s, cumulative)
CALC.expondist(x, lambda, cumulative)
CALC.forecast(x, known_y_values, known_x_values)
CALC.frequency(data_array, bins_array)
CALC.hypgeomdist(sample_s, number_sample, population_s, number_population)
CALC.intercept(arrayY, arrayX)
CALC.negbinomdist(number_f, number_s, probability_s)
CALC.percentrank(array, x, significance)
CALC.permut(number, number_chosen)
CALC.poisson(x, mean, cumulative)
CALC.prob(arrayx, arrayprob, lower_limit, upper_limit)
CALC.rank(number, array, order)
CALC.rsq(known_y_values, known_x_values)
Returns the Fisher transformation at numeric value x. This transformation produces a function that is normally distributed rather than skewed. Use this function to perform hypothesis testing on the correlation coefficient.
Example
CALC.fisher(0.75);
// returns 0.97295
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