def pearson_correlation(x, y, n):
    mean_x = sum([i for i in x]) / len(x)
    mean_y = sum([i for i in y]) / len(y)
    
    sum_xy = sum([x[i] * y[i] for i in range(n)])
    
    Sx = pow(sum([pow(i - mean_x, 2) for i in x]) / (n-1), 0.5)
    Sy = pow(sum([pow(i - mean_y, 2) for i in y]) / (n-1), 0.5)
    
    corr = (sum_xy - n * mean_x * mean_y) / ((n-1) * Sx * Sy)
    
    return corr
    
n = int(input())
data = [list(map(float, input().split())) for i in range(n)]

math = [data[i][0] for i in range(n)]
physics = [data[i][1] for i in range(n)]
chem = [data[i][2] for i in range(n)]

print("%.2f" % pearson_correlation(math, physics, n))
print("%.2f" % pearson_correlation(physics, chem, n))
print("%.2f" % pearson_correlation(chem, math, n))

from math import sqrt
x = int(input())
scores = [list(map(lambda a : float(a), input().split('\t'))) for _ in range(x)]
m = list(map(lambda a : a[0], scores))
p = list(map(lambda a : a[1], scores))
c = list(map(lambda a : a[2], scores))

def correlation(X, Y, N):
    numerator = (N * sum(map(lambda a,b : a*b, X, Y))) - (sum(X)*sum(Y))
    denominator = sqrt(N*sum(map(lambda a : a**2, X)) - (sum(X)**2)) * sqrt(N*sum(map(lambda a : a**2, Y)) - (sum(Y)**2))
    return numerator/denominator

print('%.2f'%(correlation(m,p,x)))
print('%.2f'%(correlation(p,c,x)))
print('%.2f'%(correlation(c,m,x)))

em,ep,ec = 0.0,0.0,0.0
em2,ep2,ec2 = 0.0,0.0,0.0
emp,emc,epc = 0.0,0.0,0.0
n = int(input())
for _ in range(n):
    m,p,c = map(int,input().split())
    em+=m
    ep+=p
    ec+=c
    em2+=m**2
    ep2+=p**2
    ec2+=c**2
    emp+=m*p
    emc+=m*c
    epc+=p*c

rmp=(n*emp-em*ep)/((n*em2-(em)**2)*(n*ep2-(ep)**2))**0.5
rpc=(n*epc-ec*ep)/((n*ec2-(ec)**2)*(n*ep2-(ep)**2))**0.5
rmc=(n*emc-em*ec)/((n*em2-(em)**2)*(n*ec2-(ec)**2))**0.5

print('%.2f\n%.2f\n%.2f' %(rmp,rpc,rmc))