fit an ols model with statsmodels in python

To fit an Ordinary Least Squares (OLS) model with statsmodels in Python, you can follow these steps:

1. Import the necessary libraries:

main.py
import statsmodels.api as sm
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2. Prepare your data: Assuming you have already imported your dataset and split it into the independent variable (X) and dependent variable (y), you need to add a constant to the independent variable matrix X. This is required for the intercept of the regression model. You can use sm.add_constant() for this purpose.

main.py
X = sm.add_constant(X)
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3. Fit the OLS model:

main.py
model = sm.OLS(y, X)
results = model.fit()
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4. Print the summary of the model:

main.py
print(results.summary())
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The results.summary() will provide a detailed summary that includes the coefficients, standard errors, p-values, R-squared, and other diagnostic statistics.

That's it! Now you have successfully fitted an OLS model using statsmodels in Python.