代码如下;

let XGX= {
    correlationCoefficient(pA, pB) {
        let covXY = -pA * pB
        let varX = pA * (1-pA) 
        let varY = (1-pB)* pB
        let res = covXY / (Math.sqrt(varX*varY, 2))
        return res
    },
    correlation(x,y){
        x=[0.3,50.2,99.5,199.3,299,398];
        y=[0.1,50,99.9,200,300,400];
        // 计算均值
        const avgX=x.reduce((a,b)=>a+b,0)/x.length;
        const avgY=y.reduce((a,b)=>a+b,0)/y.length;
        //计算差值
        const diffX=x.map((value,index)=>value-avgX);
        const diffY=y.map((value,index)=>value-avgY);
        //计算乘积
        const product =diffX.map((value,index)=>value*diffY[index]);
        //计算相关系数
        const sumProduct=product.reduce((a,b)=>a+b,0);
        const correlation=sumProduct/(x.length*y.length);
        return correlation
    },
    /**
     * 计算皮尔逊相关系数
     * @list1:数字列表1
     * @list2:数字列表2
     * @return number
    * */
    pearson(list1,list2){
        let newList = [];
        let len = list1.length;
        for(let i=0;i<len;i++){newList.push(list1[i]*list2[i]);}
        let mean1 = this.mean(list1),mean2=this.mean(list2);
        let cov = this.mean(newList)-mean1*mean2;
        return cov/(this.std(list1,mean1)*this.std(list2,mean2))
    },
    /**
     * 计算平均值
     * @list:数字列表
     * @return number
     * */
    mean(list){
        let sum = 0;
        list.forEach((item)=>{sum+=item})
        return sum/list.length;
    },
    /**
     * 计算标准差
     * @list:数字列表
     * @mean:列表list的平均值
     * @return number
     * */
    std(list,mean){
        let sum =0;
        list.forEach((item)=>{sum+=Math.pow(item-mean,2)})
        return Math.sqrt(sum/list.length)
    }
};
export default XGX

调用：

 效果：