Membuat distribusi sampling

Sampling di Python

James Chapman

Curriculum Manager, DataCamp

Kode sama, jawaban berbeda

coffee_ratings.sample(n=30)['total_cup_points'].mean()

82.53066666666668

coffee_ratings.sample(n=30)['total_cup_points'].mean()

81.97566666666667

coffee_ratings.sample(n=30)['total_cup_points'].mean()

82.68

coffee_ratings.sample(n=30)['total_cup_points'].mean()

81.675

Kode sama, 1000 kali

mean_cup_points_1000 = []

for i in range(1000):
    mean_cup_points_1000.append(
        coffee_ratings.sample(n=30)['total_cup_points'].mean()
    )

print(mean_cup_points_1000)

[82.11933333333333, 82.55300000000001, 82.07266666666668, 81.76966666666667, 
...
 82.74166666666666, 82.45033333333335, 81.77199999999999, 82.8163333333333]

Distribusi mean sampel untuk ukuran 30

import matplotlib.pyplot as plt
plt.hist(mean_cup_points_1000, bins=30)
plt.show()

Distribusi sampling adalah distribusi replikasi dari estimasi titik.

Histogram mean sampel.

Ukuran sampel berbeda

Ukuran sampel: 6

Histogram mean sampel dengan ukuran sampel enam.

Ukuran sampel: 150

Histogram mean sampel dengan ukuran sampel 150.

Ayo berlatih!

Sampling di Python