Fit OLS, GLM, logistic, and ARIMA statistical models in Python.
npx skills add https://github.com/sanand0/scientific-research --skill statsmodels-sanand0
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/statsmodels Command: npx skills add https://github.com/sanand0/scientific-research --skill statsmodels-sanand0
This Skill provides a comprehensive suite of tools for rigorous statistical modeling, inference, and analysis, enabling users to derive meaningful insights from data.
Fit a linear regression model using Ordinary Least Squares with y as the dependent variable and x1, x2 as independent variables.
High-intent search queries and answers about installing and using this skill.
To fit an ordinary least squares (OLS) linear regression model, you define your dependent variable and independent variables, then apply the statistical modeling toolkit to estimate coefficients and generate publication-ready diagnostic tables.
Yes, this toolkit supports time series analysis and forecasting future prices by fitting models like ARIMA, enabling you to predict economic data trends while generating confidence intervals for the forecast.
The statistical inference capabilities include rigorous hypothesis testing, calculating confidence intervals, and running model diagnostics to validate econometric and scientific research findings.
Yes, the statistical modeling toolkit supports generalized linear models (GLM) and logistic regression, allowing you to estimate relationships between variables and perform inference across a wide range of methods.
The best way to run econometric modeling and generate publication-ready statistical tables is to use this Python toolkit, which provides robust estimation, hypothesis testing, and diagnostics for scientific research.
Limitations of fitting ARIMA models for time series forecasting depend on data stationarity and proper parameter selection, requiring rigorous model diagnostics and hypothesis testing to ensure valid inference.
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