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乱数生成器の乱数をピアソンのカイ二乗検定で検定
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// 参考 | |
// http://blog.amedama.jp/entry/2016/11/20/173932 | |
// http://www.koka.ac.jp/morigiwa/sjs/chi-square_distribution.htm | |
//ピアソンのカイ二乗検定 | |
//http://blog.amedama.jp/entry/2016/11/20/173932 | |
//npi >= 10 | |
var pi = Xorshift.MAX_VALUE; | |
var n = 10000; //標本数 var dist = 10; | |
var g = new Xorshift(); | |
var array = []; | |
for(var i = 0; i < dist; i++){ | |
array[i] = 0; | |
} | |
for(var i = 0; i < n; i++){ | |
var index = Math.floor((g.rand() / Xorshift.MAX_VALUE) * 10); | |
array[index] += 1; | |
} | |
console.log(array); | |
var o = array; | |
var e = []; //期待値 | |
for(var i = 0; i < dist; i++){ | |
e[i] = n / dist; | |
} | |
console.log(e); | |
// 標本から期待値を引いて二乗すると、公式の分母部分が計算 | |
var b = o.map(function(oi, index){ | |
var ei = e[index]; | |
return Math.pow(oi - ei, 2); | |
}) | |
console.log(b); | |
var s = b.reduce(function(a, b){ | |
return a + b; | |
}); | |
console.log(s); | |
console.log(s / 10000); | |
//df = dist - 1 = 9 |
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