def logNorm(self, step): if len(self.data) >= self.maxNumb: self.data = self.data[:self.maxNumb - 1] self.data.insert(0, float(step)) else: self.data.insert(0, float(step)) dist = NormalDist.from_samples(self.data) return (step - dist.mean)/dist.stdev
def confidence_interval(data, confidence=0.95): dist = NormalDist.from_samples(data) z = NormalDist().inv_cdf((1 + confidence) / 2.) h = dist.stdev * z / ((len(data) - 1)**.5) return dist.mean, round((2 * h) / dist.mean, 4)
def calculate_confidence_interval(data, confidence=0.95): dist = NormalDist.from_samples(data) z = NormalDist().inv_cdf((1 + confidence) / 2.) h = dist.stdev * z / ((len(data) - 1)**.5) return dist.mean - h, dist.mean + h