#------------------------------------------
#Bootstrap test for significance
#------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
x = compositeL # some numpy vector
y = compositeF # another numpy vector
# we will fill this vector with the regression coefficient as we find them
b1_vector = np.zeros(5000)
#bootstraping
for i in np.arange(5000):
index = np.random.randint(0,len(x),len(x))
#pull out the (x,y) pair for each index
sample_x = x[index]
sample_y = y[index]
#calculate the b1 value and store it in the b_vector
b_values = np.polyfit(sample_x,sample_y,1)
#b1 is the first element result of polyfit
b1=b_values[0]
b1_vector[i]=b1
plt.figure(6)
plt.hist(b1_vector,50)
In this example there is 99.96% confidence that the regression coefficient is positive.
