[Python] How do I write a python code for Linear regression with omitted variables?

Generate random data for a linear regression with omitted variables and make two independent variables that are correlated with each other and make one dependent variable that is correlated with both independent variables.

Is this correct approach?

import numpy as np
import pandas as pd

# Generate two independent variables that are correlated with each other

x1 = np.random.randn(1000)
x2 = np.random.randn(1000)

# Set the correlation between x1 and x2 to 0.5
np.corrcoef(x1, x2)[0, 1] = 0.5

# Generate a dependent variable that is correlated with both independent variables
y = 0.5 * x1 + 0.25 * x2 + np.random.randn(1000)

# Create a Pandas DataFrame
df = pd.DataFrame({'x1': x1, 'x2': x2, 'y': y})

# Print the DataFrame
print(df)

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