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Statsmodels Regression Results: 5 Functions for Better Answers

Go beyond a regression coefficient table with five statsmodels methods for parameter uncertainty, covariance-adjusted inference, influential cases, and nested-model comparisons.
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After fitting a regression in statsmodels, five methods help you look beyond the coefficient table: summary() for an overview, conf_int() for parameter uncertainty, get_robustcov_results() for covariance-adjusted inference, get_influence() for observation sensitivity, and compare_lr_test() for comparing nested models. These provide diagnostic and inferential clues—not proof that a relationship is causal.

Start with the fitted results overview

summary(): What does the model report at a glance?

Once a model is fitted, call summary() on its results object to inspect the main estimates and model information:

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res = model.fit()
print(res.summary())

Statsmodels presents the summary as tables and text. Its alpha argument controls the significance level used for confidence intervals, while slim can reduce diagnostic information. The getting-started guide demonstrates the basic workflow of specifying a model, fitting it, and inspecting the fitted results with print(res.summary()) (statsmodels getting started; summary method reference).

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Treat this as an orientation, not a complete diagnostic report. A tidy summary cannot establish that the specification is appropriate or explain whether a particular observation or assumption is driving the result.

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Check uncertainty around individual estimates

conf_int(): How precise are the parameter estimates?

Use conf_int() on the fitted results to obtain confidence intervals for the model parameters:

intervals = res.conf_int()
print(intervals)

Read each interval alongside its coefficient. It describes uncertainty in the estimate under the fitted model and its assumptions; it does not guarantee that the model is correctly specified or that a coefficient has a causal interpretation. The statsmodels results reference documents the method.

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See whether inference depends on the covariance estimate

get_robustcov_results(): Does the uncertainty estimate change?

This method creates a new results instance with a robust covariance estimate as its default. Specify a covariance type suited to the data feature you are addressing, then inspect the new results:

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robust_res = res.get_robustcov_results(cov_type="HC3")
print(robust_res.summary())

The example uses HC3, a heteroskedasticity-consistent covariance choice. Choose and report the covariance type deliberately: a robust estimate changes estimated uncertainty and therefore can change standard errors, confidence intervals, and tests. It does not repair omitted variables, an incorrect functional form, or confounding. See the statsmodels covariance-results reference for the method and available options.

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Investigate observations that may drive the fit

get_influence(): Could particular cases materially affect the model?

For an OLS results object, get_influence() returns an OLSInfluence object with measures used to examine outlier behavior and observation influence:

influence = res.get_influence()
studentized = influence.resid_studentized_external
leverage = influence.hat_matrix_diag
cooks_distance = influence.cooks_distance

Studentized residuals, leverage, and Cook’s-distance-based plots can help reveal cases worth reviewing. A large residual, high leverage, or an influential-looking point is a cue to investigate the observation, model assumptions, and sensitivity of the result—not an automatic deletion rule. Check for data errors, understand why the case differs, and consider whether conclusions change when it is handled appropriately. The OLS influence method reference and official diagnostic plots notebook demonstrate these measures and plots.

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Compare nested models with a likelihood-ratio test

compare_lr_test(): Does the less-restricted model improve fit enough to reject the restricted one?

Use this method to compare a restricted model with an appropriate less-restricted, nested model. The comparison tests whether the restricted model is correct; it is not a general comparison for unrelated model specifications. For example:

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lr_stat, p_value, df_diff = restricted_res.compare_lr_test(unrestricted_res)

Report the likelihood-ratio statistic, degrees of freedom, and p-value, and state which models were compared. The p-value addresses the test under its assumptions; it is not an effect-size measure or evidence of causation. Check the statsmodels likelihood-ratio comparison reference for the method’s requirements and return values.

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Choose the method that matches the question

Method Question answered What it cannot establish
summary() What are the main fitted results and model details? That the specification is sound or that all relevant diagnostics have been checked.
conf_int() How uncertain are the parameter estimates under this fitted model? That the model assumptions or causal interpretation are valid.
get_robustcov_results() How does inference look with a different covariance estimate? That specification problems or confounding have been fixed.
get_influence() Could particular observations affect the fit? That a flagged observation is erroneous or should be removed.
compare_lr_test() Does a less-restricted nested model improve fit enough to reject the restricted model? Practical importance or causation.

These methods are complementary rather than competing. Use the one that matches your question, and combine them when you need to examine parameter uncertainty, inference, case sensitivity, and model structure.

Use diagnostics to investigate assumptions, not certify a model

Statsmodels’ regression diagnostics example covers tests for residual normality (Omni and Jarque–Bera), autocorrelation (Durbin–Watson and Breusch–Godfrey), heteroskedasticity (Breusch–Pagan and Goldfeld–Quandt), linearity (Harvey–Collier), and influence. Its notebook also demonstrates residual-versus-fitted, normal-quantile, scale-location, leverage, and variance-inflation-factor (VIF) views. Patterns can suggest nonlinearity, nonnormal residuals, nonconstant variance, leverage, or influential observations, but visual rules of thumb are not universal thresholds. Use a plot or test to investigate a stated assumption, not as a stand-alone verdict that a model is good or bad. The documentation notes that many tests return tuples without annotations; consult the relevant method documentation to interpret each result (regression diagnostics example; diagnostic plots notebook).

The stable statsmodels documentation identifies version 0.15.0. The detailed summary-method reference linked above is labeled 0.14.6, so check the documentation for your installed version if method options differ.

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