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statsmodels — statistical modeling with regression, time series, inference, and diagnostics

OSS scale score 302.2OSS health 89Update pace 336 commits/30d
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Stars
11,635
Primary language
Python
License
BSD-3-Clause
Repository last updated
Sep 17, 2026

Overview

statsmodels provides statistical modeling and econometrics in Python, including linear and generalized linear models, time-series methods, discrete models, robust estimation, and hypothesis testing. It emphasizes inferential outputs such as coefficients, standard errors, confidence intervals, statistical tests, and diagnostics.

Features and best fit

Based on official documentation; not hands-on tested · Content checked:

Inspect model estimates and statistical inference

OLS, GLS, WLS, GLMs, Logit/Probit, and related models expose coefficient estimates together with standard errors, tests, and confidence intervals.

Sources: [2]

Cover time-series and econometric modeling

ARIMA/ARIMAX, VAR, state-space models, exponential smoothing, unit-root tests, cointegration, and other time-series tools are available in the same package.

Sources: [2]

For analysis where explanation, testing, and diagnostics matter

statsmodels fits workflows that need interpretable effects, formal hypothesis tests, and model diagnostics in addition to or instead of predictive performance.

Sources: [2][4]

Do not reduce model quality to statistical significance alone

P-values and fit statistics need to be interpreted alongside assumptions, residual diagnostics, effect size, data-generating context, and external validation. The README also labels sandbox code as not production ready.

Sources: [2]

Official sources

  1. [1]statsmodels/statsmodels repository(2026-09-20)
  2. [2]statsmodels README(2026-09-20)
  3. [3]statsmodels BSD-3-Clause license(2026-09-20)
  4. [4]statsmodels documentation(2026-09-20)
Supplemental curator note

statsmodels is especially useful when analysis needs coefficient estimates, standard errors, p-values, confidence intervals, and model diagnostics rather than prediction accuracy alone. It complements predictive ML tooling with inferential statistical modeling.

Try it in 3 steps

  1. 1

    Install statsmodels

    Install the released package in a virtual environment.

    python -m pip install statsmodels
  2. 2

    Fit an OLS regression

    Fit OLS and inspect a summary that includes coefficients, standard errors, and statistical tests.

    python -c "import statsmodels.api as sm; y=[1,2,3,4]; X=sm.add_constant([1,2,3,4]); print(sm.OLS(y,X).fit().summary())"
  3. 3

    Inspect inference results

    Look beyond predictions and examine coefficient estimates, p-values, and confidence intervals.

    Inspect result.params, result.pvalues, result.conf_int()
Check the official README

Growth

Growth trends · Last 30 days

11,635 Stars

Trend data is still being collected.

Development activity

Last 90 days · weekly

Commits (last 30 days)
336
Open PRs
178
Issues opened
29
Issues closed
155
PRs opened
377
PRs merged
371

Issues

29 / 155

Jun 23Sep 20
Issues openedIssues closed

Pull requests

377 / 371

Jun 23Sep 20
PRs openedPRs merged

Maintenance

Median first response
2.8 hr
Issue response rate
37.9% (11/29)

Based on up to the 100 newest issues opened by external users in the last 90 days. A first comment from an OWNER, MEMBER, or COLLABORATOR counts as a response; issues whose full comment history cannot be checked are excluded. The median and response rate update weekly.

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GitHub data

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GitHub Topics

  • python
  • statistics
  • econometrics
  • data-analysis
  • generalized-linear-models
  • timeseries-analysis
  • regression-models
  • count-model
  • data-science
  • forecasting
  • hypothesis-testing
  • prediction
Stars
11,635
Forks
3,601
Watchers
287
Open issues
2,644
Contributors
397
Primary language
Python
Repository last updated
Sep 17, 2026
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