X = [95, 85, 80, 70, 60]
Y = [85, 95, 70, 65, 70]

import math

class JStatistics():
    @staticmethod
    def vectorial_operations(X, Y, operator):
        if type(Y)==type(1):
            Y=[Y for _ in X]
        assert len(X)==len(Y)
        if operator=="-":
            return [X[i] - Y[i] for i in range(len(X))] 
        elif operator=="+":
            return [X[i] + Y[i] for i in range(len(X))] 
        elif operator=="*":
            return [X[i] * Y[i] for i in range(len(X))] 
        elif operator=="**":
            return [X[i] ** Y[i] for i in range(len(X))] 
        elif operator=="/":
            return [X[i] / Y[i] for i in range(len(X))] 
        
        raise Exception("Operation not managed yet")
    
    @staticmethod
    def mean(v):
        return sum(v)/len(v)

    @staticmethod
    def variance(arr):
        mi = JStatistics.mean(arr)
        arr_minus_mi_pow2 = [math.pow(x-mi,2) for x in arr]
        return JStatistics.mean(arr_minus_mi_pow2)
        
    @staticmethod
    def stdDev(arr):
        return math.sqrt(JStatistics.variance(arr))
        
    @staticmethod
    def covariance(X, Y):
        assert len(X)==len(Y)
        n = len(X)
        xBar, yBar = JStatistics.mean(X), JStatistics.mean(Y)
        cov = (1/n)*sum( (X[i]-xBar)*(Y[i]-yBar) for i in range(n) )
        return cov
        
    @staticmethod
    def coefficientPearson(X, Y):
        xstd = JStatistics.stdDev(X)
        ystd = JStatistics.stdDev(Y)
        return JStatistics.covariance(X, Y)/(xstd*ystd)
    
    @staticmethod
    def regressionLine(X, Y):
        m = JStatistics.coefficientPearson(X,Y)*(JStatistics.stdDev(Y)/JStatistics.stdDev(X))
        q = JStatistics.mean(Y) - m * JStatistics.mean(X)
        return m,q
    
m,q = JStatistics.regressionLine(X,Y)
y_hat = m*80+q
print(round(y_hat,3))