### Question:
ContinuousDistributions { public static double gaussCdf(double z) { double y; double p; double w; if (z == 0.0) { p = 0.0; } else { y = Math.abs(z) / 2.0; if (y >= 3.0) { p = 1.0; } else if (y < 1.0) { w = y * y; p = ((((((((0.000124818987 * w - 0.001075204047) * w + 0.005198775019) * w - 0.019198292004) * w + 0.059054035642) * w - 0.151968751364) * w + 0.319152932694) * w - 0.531923007300) * w + 0.797884560593) * y * 2.0; } else { y = y - 2.0; p = (((((((((((((-0.000045255659 * y + 0.000152529290) * y - 0.000019538132) * y - 0.000676904986) * y + 0.001390604284) * y - 0.000794620820) * y - 0.002034254874) * y + 0.006549791214) * y - 0.010557625006) * y + 0.011630447319) * y - 0.009279453341) * y + 0.005353579108) * y - 0.002141268741) * y + 0.000535310849) * y + 0.999936657524; } } if (z > 0.0) { return (p + 1.0) / 2.0; } return (1.0 - p) / 2.0; }  static double chisquareCdf(double x, int df); static double gaussCdf(double z); static double gamma(double x); static double logGamma(double Z); static double studentsCdf(double x, int df); static double exponentialCdf(double x, double lamda); static double betaCdf(double x, double a, double b); static double fCdf(double x, int f1, int f2); static double gammaCdf(double x, double a, double b); static double uniformCdf(double x, double a, double b); static double kolmogorov(double z); static double gaussInverseCdf(double p); static double chisquareInverseCdf(double p, int df); static double normalQuantile(double p, double mu, double sigma); static double dirichletPdf(double[] pi, double[] ai); static double dirichletPdf(double[] pi, double a); static double[] multinomialGaussianSample(double[] mean, double[][] covariance); static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x);  }### Answer:
@Test public void testGaussCdf() { logger.info("GaussCdf"); double z = 3.0; double expResult = 0.9986501025724; double result = ContinuousDistributions.gaussCdf(z); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }