from scipy.interpolate import chow_test

`scipy.interpolate.chow_test` is a function that performs a Chow test for structural change in a linear regression model. The Chow test is used to determine whether two linear regressions on different data sets are significantly different from each other.

Here is an example of how to use `chow_test`:

import numpy as np
from scipy.interpolate import chow_test

# Generate some sample data
x = np.linspace(0, 10, 100)
y1 = 2*x + np.random.normal(0, 1, 100)
y2 = 5*x + np.random.normal(0, 1, 100)

# Combine the data
x_combined = np.concatenate((x, x))
y_combined = np.concatenate((y1, y2))

# Find the index where the data sets change
index = len(x)-1

# Perform the Chow test
F, p = chow_test(x_combined[:, np.newaxis], y_combined, [index])
print("F-statistic: ", F)
print("p-value: ", p)

In this example, we generate two linear regression models (`y1` and `y2`) with different slopes on different data sets. We then combine the data sets and use `chow_test` to determine if there is a significant difference between the two regression models. The resulting F-statistic and p-value can be used to make this determination.