Fit statistical models and run diagnostics in Python with statsmodels.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statsmodels-jasrajtulsi
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/statsmodels Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statsmodels-jasrajtulsi
This Skill removes the complexity of choosing, fitting, and validating statistical models so you can move from raw data to defensible conclusions faster.
Ask the assistant to use statsmodels to fit the right statistical model for your dataset, run the relevant diagnostics, and summarize the results in plain language.
High-intent search queries and answers about installing and using this skill.
To run linear regression with robust standard errors, fit your model and generate coefficient tables with confidence intervals. This approach validates assumptions and quantifies relationships while keeping your statistical workflow organized and reproducible.
Forecasting time series requires fitting statistical models and running residual diagnostics to test assumptions. This process estimates effects, generates prediction intervals, and ensures defensible conclusions for demand forecasting or survey analysis.
Yes, you can fit generalized linear models and discrete choice models for various data types. The workflow involves model fitting, comparing models, and reporting coefficients with confidence intervals to quantify relationships accurately.
Testing statistical assumptions involves running residual diagnostics and comparing models after the initial fit. This validates your model by checking assumptions, inspecting residuals, and reporting coefficients with robust standard errors.
Yes, this statistical modeling approach quantifies treatment effects in experiments by fitting rigorous models. It provides clear inference through coefficient tables, confidence intervals, and hypothesis testing workflows for defensible conclusions.
Avoid generalized linear models when residual diagnostics reveal violated assumptions or when prediction intervals are unreliable. If model fitting fails residual checks or robust standard errors indicate poor fit, consider alternative statistical approaches.
jimmc414Fit OLS, GLM, ARIMA, and other statistical models in Python.
jkitchinGuide regression analysis and outlier detection in Python using statsmodels, scikit-learn, scipy, and PyOD.
SciMate-AIModel relationships in data using statsmodels linear, GLM, and time-series tools.
ovachieverEstimate regression and time-series models for statistical inference.
