ols with statsmodels in python

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

1. Import the necessary libraries:

main.py
import statsmodels.api as sm
import pandas as pd
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2. Create your data frame with the dependent variable and independent variables:

main.py
data = {'x1': [1, 2, 3, 4, 5],
        'x2': [6, 7, 8, 9, 10],
        'y': [11, 12, 13, 14, 15]}
df = pd.DataFrame(data)
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3. Add a constant column to the data frame if you want to include an intercept in the regression model:

main.py
df['const'] = 1
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4. Specify the dependent and independent variables for the regression:

main.py
X = df[['const', 'x1', 'x2']]  # independent variables
y = df['y']  # dependent variable
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5. Fit the OLS model:

main.py
model = sm.OLS(y, X)
results = model.fit()
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6. Get the summary of the regression results:

main.py
print(results.summary())
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This will display detailed information about the OLS regression analysis, including coefficients, standard errors, t-statistics, p-values, and more.

Make sure you have the necessary libraries installed by running pip install pandas statsmodels.

Remember to adjust the variable names and data frame as per your specific dataset.