chaipi-chaya

import numpy as np
import random
import matplotlib.pyplot as plt

# Step 1
## show that as the sample size increases the mean of sample is close to population mean
# build gamma distribution as population
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
mu = shape*scale # mean
s = np.random.gamma(shape, scale, 1000000)

chaipi-chaya

import numpy as np
import random
import matplotlib.pyplot as plt

# Step 1
## show that as the sample size increases the mean of sample is close to population mean
# build gamma distribution as population
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
s = np.random.gamma(shape, scale, 1000000)

chaipi-chaya

## expect value of sample
import numpy as np

# use last sampling
from diffSampleSize import meansample
# mean and standard deviation from population
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
sample = meansample[5]
# expected value of sample equal to expect value of population
print("expected value of sample:", np.mean(sample))

chaipi-chaya

import numpy as np
import random
import matplotlib.pyplot as plt

# build gamma distribution as population
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
s = np.random.gamma(shape, scale, 1000000)

# Step 1
## sample with different sample size

chaipi-chaya

## standardize part
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats

# Step 1
# use last sampling
from diffNumSampling import meansample
sm = meansample[len(meansample)-1]

chaipi-chaya

import numpy as np
import random
import matplotlib.pyplot as plt

# Step 1
# create a population with a gamma distribution
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
mu = shape*scale # mean and standard deviation
sigma = scale*np.sqrt(shape)
s = np.random.gamma(shape, scale, 1000000)