third_party/blink/perf_tests/resources/statistics.js - chromium/src - Git at Google

 /*
  * Copyright (C) 2012, 2013 Apple Inc. All rights reserved.
  *
  * Redistribution and use in source and binary forms, with or without
  * modification, are permitted provided that the following conditions
  * are met:
  * 1. Redistributions of source code must retain the above copyright
  *    notice, this list of conditions and the following disclaimer.
  * 2. Redistributions in binary form must reproduce the above copyright
  *    notice, this list of conditions and the following disclaimer in the
  *    documentation and/or other materials provided with the distribution.
  *
  * THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS ``AS IS''
  * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
  * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
  * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS
  * BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
  * THE POSSIBILITY OF SUCH DAMAGE.
  */

 var Statistics = new (function () {

     this.max = function (values) {
         var maxVal = values[0];
         for (var i = 1; i < values.length; i++) {
             maxVal = Math.max(maxVal, values[i]);
         }
         return maxVal;
     }

     this.min = function (values) {
         var minVal = values[0];
         for (var i = 1; i < values.length; i++) {
             minVal = Math.min(minVal, values[i]);
         }
         return minVal;
     }

     this.sum = function (values) {
         return values.reduce(function (a, b) { return a + b; }, 0);
     }

     this.squareSum = function (values) {
         return values.reduce(function (sum, value) { return sum + value * value;}, 0);
     }

     // With sum and sum of squares, we can compute the sample standard deviation in O(1).
     // See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
     this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
         if (numberOfSamples < 2)
             return 0;
         return Math.sqrt(squareSum / (numberOfSamples - 1)
             - sum * sum / (numberOfSamples - 1) / numberOfSamples);
     }

     this.supportedConfidenceLevels = function () {
         var supportedLevels = [];
         for (var quantile in tDistributionInverseCDF)
             supportedLevels.push((1 - (1 - quantile) * 2).toFixed(2));
         return supportedLevels;
     }

     // Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified confidence level in O(1).
     this.confidenceIntervalDelta = function (confidenceLevel, numberOfSamples, sum, squareSum) {
         var probability = (1 - (1 - confidenceLevel) / 2);
         if (!(probability in tDistributionInverseCDF)) {
             console.warn('We only support ' + this.supportedConfidenceLevels().map(
                 function (level) { return level * 100 + '%'; } ).join(', ') + ' confidence intervals.');
             return NaN;
         }
         if (numberOfSamples < 2)
             return Number.POSITIVE_INFINITY;

         var cdfForProbability = tDistributionInverseCDF[probability];
         var degreesOfFreedom = numberOfSamples - 1;

         // tDistributionQuantile(degreesOfFreedom, confidenceLevel) * sampleStandardDeviation / sqrt(numberOfSamples) * S/sqrt(numberOfSamples)
         if (degreesOfFreedom <= 100)
           var quantile = cdfForProbability[degreesOfFreedom - 1]; // The first entry is for the one degree of freedom.
         else if (degreesOfFreedom <= 300)
           var quantile = cdfForProbability[Math.round(degreesOfFreedom / 10) + 100 - 10 - 1];
         else if (degreesOfFreedom <= 1300)
           var quantile = cdfForProbability[Math.round(degreesOfFreedom / 100) + 120 - 3 - 1];
         else
           var quantile = cdfForProbability[cdfForProbability.length - 1];
         return quantile * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
     }

     this.confidenceInterval = function (values, probability) {
         var sum = this.sum(values);
         var mean = sum / values.length;
         var delta = this.confidenceIntervalDelta(probability || 0.95, values.length, sum, this.squareSum(values));
         return [mean - delta, mean + delta];
     }

     // See http://en.wikipedia.org/wiki/Student's_t-distribution#Table_of_selected_values
     // This table contains one sided (a.k.a. tail) values.
     // Use TINV((1 - probability) * 2, df) in your favorite spreadsheet software to compute these.
     // The spacing of the values with df greater than 100 maintains error less than 0.8%.
     var tDistributionInverseCDF = {
         0.9: [
             // 1 - 100 step 1
             3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
             1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
             1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
             1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
             1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
             1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
             1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
             1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
             1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
             1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075,
             // 110 - 300 step 10
             1.289295, 1.288646, 1.288098, 1.287628, 1.287221, 1.286865, 1.286551, 1.286272, 1.286023, 1.285799,
             1.285596, 1.285411, 1.285243, 1.285089, 1.284947, 1.284816, 1.284695, 1.284582, 1.284478, 1.284380,
             // 400 - 1300 step 100
             1.283672, 1.283247, 1.282964, 1.282762, 1.282611, 1.282493, 1.282399, 1.282322, 1.282257, 1.282203,
             // Infinity
             1.281548],
         0.95: [
             // 1 - 100 step 1
             6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
             1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
             1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
             1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
             1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
             1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
             1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
             1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
             1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
             1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234,
             // 110 - 300 step 10
             1.658824, 1.657651, 1.656659, 1.655811, 1.655076, 1.654433, 1.653866, 1.653363, 1.652913, 1.652508,
             1.652142, 1.651809, 1.651506, 1.651227, 1.650971, 1.650735, 1.650517, 1.650314, 1.650125, 1.649949,
             // 400 - 1300 step 100
             1.648672, 1.647907, 1.647397, 1.647033, 1.646761, 1.646548, 1.646379, 1.646240, 1.646124, 1.646027,
             // Infinity
             1.644847],
         0.975: [
             // 1 - 100 step 1
             12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
             2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
             2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
             2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
             2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
             2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
             1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
             1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
             1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
             1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972,
             // 110 - 300 step 10
             1.981765, 1.979930, 1.978380, 1.977054, 1.975905, 1.974902, 1.974017, 1.973231, 1.972528, 1.971896,
             1.971325, 1.970806, 1.970332, 1.969898, 1.969498, 1.969130, 1.968789, 1.968472, 1.968178, 1.967903,
             // 400 - 1300 step 100
             1.965912, 1.964720, 1.963926, 1.963359, 1.962934, 1.962603, 1.962339, 1.962123, 1.961943, 1.961790,
             // Infinity
             1.959964],
         0.99: [
             // 1 - 100 step 1
             31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
             2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
             2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
             2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
             2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
             2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
             2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
             2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
             2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
             2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217,
             // 110 - 300 step 10
             2.360726, 2.357825, 2.355375, 2.353278, 2.351465, 2.349880, 2.348483, 2.347243, 2.346134, 2.345137,
             2.344236, 2.343417, 2.342670, 2.341985, 2.341356, 2.340775, 2.340238, 2.339739, 2.339275, 2.338842,
             // 400 - 1300 step 100
             2.335706, 2.333829, 2.332579, 2.331687, 2.331018, 2.330498, 2.330083, 2.329743, 2.329459, 2.329220,
             // Infinity
             2.326348],
     };

 })();

 if (typeof module != 'undefined') {
     for (var key in Statistics)
         module.exports[key] = Statistics[key];
 }
	/*
	* Copyright (C) 2012, 2013 Apple Inc. All rights reserved.
	*
	* Redistribution and use in source and binary forms, with or without
	* modification, are permitted provided that the following conditions
	* are met:
	* 1. Redistributions of source code must retain the above copyright
	* notice, this list of conditions and the following disclaimer.
	* 2. Redistributions in binary form must reproduce the above copyright
	* notice, this list of conditions and the following disclaimer in the
	* documentation and/or other materials provided with the distribution.
	*
	* THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS ``AS IS''
	* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
	* THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
	* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS
	* BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
	* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
	* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
	* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
	* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
	* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
	* THE POSSIBILITY OF SUCH DAMAGE.
	*/

	var Statistics = new (function () {

	this.max = function (values) {
	var maxVal = values[0];
	for (var i = 1; i < values.length; i++) {
	maxVal = Math.max(maxVal, values[i]);
	}
	return maxVal;
	}

	this.min = function (values) {
	var minVal = values[0];
	for (var i = 1; i < values.length; i++) {
	minVal = Math.min(minVal, values[i]);
	}
	return minVal;
	}

	this.sum = function (values) {
	return values.reduce(function (a, b) { return a + b; }, 0);
	}

	this.squareSum = function (values) {
	return values.reduce(function (sum, value) { return sum + value * value;}, 0);
	}

	// With sum and sum of squares, we can compute the sample standard deviation in O(1).
	// See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
	this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
	if (numberOfSamples < 2)
	return 0;
	return Math.sqrt(squareSum / (numberOfSamples - 1)
	- sum * sum / (numberOfSamples - 1) / numberOfSamples);
	}

	this.supportedConfidenceLevels = function () {
	var supportedLevels = [];
	for (var quantile in tDistributionInverseCDF)
	supportedLevels.push((1 - (1 - quantile) * 2).toFixed(2));
	return supportedLevels;
	}

	// Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified confidence level in O(1).
	this.confidenceIntervalDelta = function (confidenceLevel, numberOfSamples, sum, squareSum) {
	var probability = (1 - (1 - confidenceLevel) / 2);
	if (!(probability in tDistributionInverseCDF)) {
	console.warn('We only support ' + this.supportedConfidenceLevels().map(
	function (level) { return level * 100 + '%'; } ).join(', ') + ' confidence intervals.');
	return NaN;
	}
	if (numberOfSamples < 2)
	return Number.POSITIVE_INFINITY;

	var cdfForProbability = tDistributionInverseCDF[probability];
	var degreesOfFreedom = numberOfSamples - 1;

	// tDistributionQuantile(degreesOfFreedom, confidenceLevel) * sampleStandardDeviation / sqrt(numberOfSamples) * S/sqrt(numberOfSamples)
	if (degreesOfFreedom <= 100)
	var quantile = cdfForProbability[degreesOfFreedom - 1]; // The first entry is for the one degree of freedom.
	else if (degreesOfFreedom <= 300)
	var quantile = cdfForProbability[Math.round(degreesOfFreedom / 10) + 100 - 10 - 1];
	else if (degreesOfFreedom <= 1300)
	var quantile = cdfForProbability[Math.round(degreesOfFreedom / 100) + 120 - 3 - 1];
	else
	var quantile = cdfForProbability[cdfForProbability.length - 1];
	return quantile * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
	}

	this.confidenceInterval = function (values, probability) {
	var sum = this.sum(values);
	var mean = sum / values.length;
	var delta = this.confidenceIntervalDelta(probability \|\| 0.95, values.length, sum, this.squareSum(values));
	return [mean - delta, mean + delta];
	}

	// See http://en.wikipedia.org/wiki/Student's_t-distribution#Table_of_selected_values
	// This table contains one sided (a.k.a. tail) values.
	// Use TINV((1 - probability) * 2, df) in your favorite spreadsheet software to compute these.
	// The spacing of the values with df greater than 100 maintains error less than 0.8%.
	var tDistributionInverseCDF = {
	0.9: [
	// 1 - 100 step 1
	3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
	1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
	1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
	1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
	1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
	1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
	1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
	1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
	1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
	1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075,
	// 110 - 300 step 10
	1.289295, 1.288646, 1.288098, 1.287628, 1.287221, 1.286865, 1.286551, 1.286272, 1.286023, 1.285799,
	1.285596, 1.285411, 1.285243, 1.285089, 1.284947, 1.284816, 1.284695, 1.284582, 1.284478, 1.284380,
	// 400 - 1300 step 100
	1.283672, 1.283247, 1.282964, 1.282762, 1.282611, 1.282493, 1.282399, 1.282322, 1.282257, 1.282203,
	// Infinity
	1.281548],
	0.95: [
	// 1 - 100 step 1
	6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
	1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
	1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
	1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
	1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
	1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
	1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
	1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
	1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
	1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234,
	// 110 - 300 step 10
	1.658824, 1.657651, 1.656659, 1.655811, 1.655076, 1.654433, 1.653866, 1.653363, 1.652913, 1.652508,
	1.652142, 1.651809, 1.651506, 1.651227, 1.650971, 1.650735, 1.650517, 1.650314, 1.650125, 1.649949,
	// 400 - 1300 step 100
	1.648672, 1.647907, 1.647397, 1.647033, 1.646761, 1.646548, 1.646379, 1.646240, 1.646124, 1.646027,
	// Infinity
	1.644847],
	0.975: [
	// 1 - 100 step 1
	12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
	2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
	2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
	2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
	2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
	2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
	1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
	1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
	1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
	1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972,
	// 110 - 300 step 10
	1.981765, 1.979930, 1.978380, 1.977054, 1.975905, 1.974902, 1.974017, 1.973231, 1.972528, 1.971896,
	1.971325, 1.970806, 1.970332, 1.969898, 1.969498, 1.969130, 1.968789, 1.968472, 1.968178, 1.967903,
	// 400 - 1300 step 100
	1.965912, 1.964720, 1.963926, 1.963359, 1.962934, 1.962603, 1.962339, 1.962123, 1.961943, 1.961790,
	// Infinity
	1.959964],
	0.99: [
	// 1 - 100 step 1
	31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
	2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
	2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
	2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
	2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
	2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
	2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
	2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
	2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
	2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217,
	// 110 - 300 step 10
	2.360726, 2.357825, 2.355375, 2.353278, 2.351465, 2.349880, 2.348483, 2.347243, 2.346134, 2.345137,
	2.344236, 2.343417, 2.342670, 2.341985, 2.341356, 2.340775, 2.340238, 2.339739, 2.339275, 2.338842,
	// 400 - 1300 step 100
	2.335706, 2.333829, 2.332579, 2.331687, 2.331018, 2.330498, 2.330083, 2.329743, 2.329459, 2.329220,
	// Infinity
	2.326348],
	};

	})();

	if (typeof module != 'undefined') {
	for (var key in Statistics)
	module.exports[key] = Statistics[key];
	}