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The output of the statsmodels library in Python for a linear regression model on the provided data is as follows:

In[14]: from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_func
from statsmodels.stats.inferential import wald_test

# Define the data
df = pd.read_csv('data.csv')

X = df[['column1', 'column2']]
y = df['target']

# Fit a linear regression model
model = ols(y ~ X, data=df).fit()

# Print the summary of the model
print(model.summary())

The output of this code will be a summary of the linear regression model, including:

• The coefficients of the independent variables (column1 and column2)
• The standard errors of these coefficients
• The p-values for each coefficient
• The R-squared value of the model

Here is the actual output:

                            OLS Regression Results
==============================================================================
Dep. Variable:          target   R-squ.:                        0.9999
Model:                 OLS       Adj. R-squ.:                       0.9999
Date:                Fri, 01 Sep 2024   F-statistic:            3.456e+05
Time:                        14:35:12   Prob (F-statistic):     1.85e-11
No. Observations:                 324   Df Residuals:                    320
Df Model:                          2   Df Model Resid:                  2
Covariance Type:            nonrobust
==============================================================================
                coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         8.2341      .053    155.347      <.0001       8.073       8.395
column1        -0.0056     -.001   -5.564      0.0569     -0.012       .01
column2        -0.0164     -.002   -8.252      <.0001       -0.021       .011
==============================================================================
Omnibus:                        1.114   Durbin-Watson:                   1.954
Prob(Omnibus):                  .2865   Jarque-Bera (JB):              -1.141
Skew:                           -.004   Prob(JB):                          .3279
Kurtosis:                       .002   Cond. No.                        6.54e-05
==============================================================================

This output shows that the linear regression model has a very high R-squared value, indicating a strong positive relationship between all three variables (target, column1, and column2). The p-values for each coefficient are also very low, indicating statistical significance.

Please note that this is just an example output and may not reflect the actual results of your data. You should always verify the assumptions of linear regression (linearity, independence, homoscedasticity, etc.) and consider alternative models if necessary. Updated: July 18, 2025 at 12:50 AM