{"enrichment":{"faq":[{"a":"Statsmodels makes OLS regression straightforward with its formula interface. Import `statsmodels.formula.api as smf`, define your model using R-style syntax (e.g., `smf.ols('y ~ x1 + x2', data=df)`), then call `.fit()` to estimate coefficients. The resulting summary includes p-values, confidence intervals, R-squared, and diagnostic statistics\u2014everything needed for publication.","q":"How do I perform OLS linear regression in Python using statsmodels?"},{"a":"Statsmodels supports OLS (ordinary least squares), GLM (generalized linear models), logistic regression, Poisson regression for count data, weighted least squares (WLS) for heteroscedastic data, and many specialized models. Each provides detailed diagnostics, p-values, and confidence intervals for rigorous statistical inference.","q":"What regression models does statsmodels support?"},{"a":"Yes. Statsmodels includes ARIMA and SARIMAX for time series analysis and forecasting. Use `statsmodels.tsa.arima.model.ARIMA` to fit autoregressive integrated moving average models, or `SARIMAX` for seasonal variants. Both support diagnostic plots, in-sample fit statistics, and out-of-sample predictions.","q":"Can statsmodels perform ARIMA time series forecasting?"},{"a":"Statsmodels delivers comprehensive hypothesis testing through model summaries that report p-values, confidence intervals, and test statistics for each coefficient. It supports t-tests, F-tests, Wald tests, and likelihood ratio tests. The `.summary()` method generates publication-ready tables with all diagnostic information.","q":"How does statsmodels handle hypothesis testing and p-values?"},{"a":"Statsmodels supports causal inference through regression-based treatment effect estimation, instrumental variables (IV/2SLS), and difference-in-differences models. These methods estimate average treatment effects while controlling for confounders, with p-values and confidence intervals for rigorous causal claims.","q":"What causal inference and treatment effect methods are available?"},{"a":"Statsmodels provides residual diagnostics and tests for model assumptions. Calculate variance inflation factors (VIF) to detect multicollinearity, use Breusch-Pagan or White tests for heteroscedasticity, and examine residual plots. The `.summary()` output includes Durbin-Watson statistics and other diagnostic metrics for comprehensive model validation.","q":"How can I check for multicollinearity and heteroscedasticity in statsmodels?"}],"shadow_tags":["statistical-inference","hypothesis-testing","time-series-modeling","causal-analysis","model-diagnostics","academic-publishing","r-style-formulas","survival-analysis","generalized-linear-models","heteroscedasticity-robust"],"summary_rewrite":"Statsmodels delivers rigorous statistical inference through comprehensive model estimation, diagnostic testing, and publication-ready summaries. It excels at linear and generalized linear regression, time series modeling, ANOVA, survival analysis, and causal inference\u2014all with the p-values and confidence intervals essential for academic and scientific work."},"files":[{"bytes":10080,"path":"skills/statsmodels/SKILL.md","sha256":"dfbda45438e88cba747dc9cc9052cc5ebb503140a621676e4409c57de662df3c","url":"https://skillfed.io/files/tondevrel/scientific-agent-skills/statsmodels/f0c425d9/SKILL.md"}],"id":"tondevrel/scientific-agent-skills/statsmodels","links":{"html":"https://skillfed.io/tondevrel/scientific-agent-skills/statsmodels","md":"https://skillfed.io/tondevrel/scientific-agent-skills/statsmodels.md","repo":"https://github.com/tondevrel/scientific-agent-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":2,"language":null,"last_updated":"2026-02-01","license":"MIT","name":"statsmodels","publisher":"tondevrel","stars":19},"relations":{"similar":[{"id":"jaechang-hits/SciAgent-Skills/statsmodels-statistical-modeling"},{"id":"foryourhealth111-pixel/Vibe-Skills/statsmodels"},{"id":"synthetic-sciences/openscience/statsmodels"},{"id":"drshailesh88/integrated_content_OS/statsmodels"},{"id":"zLanqing/codex-claude-academic-skills/statsmodels"},{"id":"LeonChaoX/qinyan-academic-skills/statsmodels"},{"id":"beita6969/ScienceClaw/statsmodels"},{"id":"beita6969/ScienceClaw/statsmodels-stats"},{"id":"K-Dense-AI/scientific-agent-skills/statsmodels"},{"id":"beita6969/ScienceClaw/data-analysis"}]},"slug":{"owner":"tondevrel","repo":"scientific-agent-skills","skill":"statsmodels"},"version":"f0c425d9"}