class LR:
    def __init__(self):
        self.slope = None
        self.intercept = None
        
    def fit(self, x, y):
        x_avg = sum(x)/x.__len__()
        y_avg = sum(y)/y.__len__()
        numerator = sum([(a - x_avg)*(b - y_avg) for a,b in zip(x,y)])
        denominator = sum([(a - x_avg)**2 for a in x])
        self.slope = round(numerator/denominator, 3)
        self.intercept = y_avg - self.slope*x_avg
    def predict(self, x):
        if self.slope and self.intercept:
            return self.slope*x + self.intercept
        else:
            raise ModuleNotFoundError('predict is not found, call fit() before predict()')
        

if __name__ == '__main__':
    if __name__ == '__main__':
        feature_1 = "Physics Scores  15  12  8   8   7   7   7   6   5   3"
        feature_2 = "History Scores  10  25  17  11  13  17  20  13  9   15"
        feature_1 = [int(x) for x in input().split() if x.isnumeric()]
        feature_2 = [int(x) for x in input().split() if x.isnumeric()]
        model = LR()
        model.fit(feature_1, feature_2)
        out = str(model.predict(10))
        out = out.split('.')
        print(float(out[0] + '.' + out[1][0]))