Builds and validates regression models (linear, polynomial) on datasets to predict outcomes, uncover relationships, and report metrics like R-squared and RMSE.
How this skill is triggered — by the user, by Claude, or both
Slash command
/regression-analysis-tool:performing-regression-analysisThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Perform regression analysis by building and validating linear, polynomial, and other regression models to uncover variable relationships and predict continuous outcomes.
Perform regression analysis by building and validating linear, polynomial, and other regression models to uncover variable relationships and predict continuous outcomes.
analyze data, build regression models, and provide insights into the relationships between variables. It leverages the regression-analysis-tool plugin to automate the process and ensure best practices are followed.
This skill activates when you need to:
User request: "Can you build a regression model to predict house prices based on square footage and number of bedrooms?"
The skill will:
User request: "I need to analyze the sales data for the past year and identify any trends using regression analysis."
The skill will:
This skill works independently using the regression-analysis-tool plugin. It can be used in conjunction with other data analysis and visualization tools to provide a comprehensive understanding of the data.
The skill produces structured output relevant to the task.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin regression-analysis-toolProvides Statsmodels API patterns for OLS, GLM, discrete choice, time series (ARIMA/SARIMAX), and diagnostics. Use for /ds:experiment model fitting/forecasting and /ds:eda VIF/stationarity checks.
Fits and interprets statistical models (OLS, GLM, logistic, ARIMA) with diagnostics, residuals, and inference tables. Best for econometrics, time series, and rigorous hypothesis testing.
Statistical modeling in Python with statsmodels: OLS, GLM, mixed models, and ARIMA with diagnostics, residuals, and inference. Use for econometrics, time series, or rigorous inference.