aula01.py

import statistics as stat
a = [1, 2, 3, 4, 4]
stat.mean(a)
stat.mean([-1.0, 2.5, 3.25, 5.75])
b = [3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 57.5]
stat.fmean([3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 57.5])
# >>> stat.fmean([3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 5.75])
# 3.3214285714285716
# >>> stat.fmean([3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 57.5])
# 10.714285714285714

# Média geométrica
stat.geometric_mean([54, 24, 36]) 
# 36.000000000000014

stat.harmonic_mean([40, 60])
2/(1/40+1/60)


stat.median([3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 5.75])
# 3.5
stat.median([3.5, 4.0, 5.25,-1.0, 2.5, 3.25, 57.5])
# 3.5
stat.median([35, 4.0, 5.25,-1.0, 2.5, 3.25, 5.75])
# 4.0

stat.median_low([1, 3, 5, 7])
stat.median_low([1, 3, 5, 7, 8])


data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
stat.pvariance(data)
# 1.1760204081632653
stat.variance(data)
# 1.3720238095238095
stat.pstdev(data)
# 1.084444746477784
stat.pvariance(data)**(1/2)
# 1.084444746477784
stat.stdev(data)
# 1.171334200612195

data = [105, 129, 87, 86, 111, 111, 89, 81, 108, 92, 110,
        100, 75, 105, 103, 109, 76, 119, 99, 91, 103, 129,
        106, 101, 84, 111, 74, 87, 86, 103, 103, 106, 86,
        111, 75, 87, 102, 121, 111, 88, 89, 101, 106, 95,
        103, 107, 101, 81, 109, 104]

# >>> stat.median(data)
# 102.5
stat.quantiles(data, n = 4)
# [87.0, 102.5, 108.25]
stat.quantiles(data, n = 10)
# [81.0, 86.2, 89.0, 99.4, 102.5, 103.6, 106.0, 109.8, 111.0]
stat.median(data)

[round(q, 1) for q in stat.quantiles(data, n=10)]
# [81.0, 86.2, 89.0, 99.4, 102.5, 103.6, 106.0, 109.8, 111.0]

stat.quantiles(data, n = 4)

dist = a[2]-a[0] # 21.25

x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
stat.covariance(x, y)
0.75
z = [9, 8, 7, 6, 5, 4, 3, 2, 1]
stat.covariance(x, z)
-7.5
stat.covariance(z, x)
-7.5

x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [9, 8, 7, 6, 5, 4, 3, 2, 1]
stat.correlation(x, x)
1.0
stat.correlation(x, y)
-1.0