Skip to content
TrustedSkillsbeta
Back to all skills

Statsmodels

Listed
davila7·vlatest·data·MIT

Statsmodels provides statistical modeling tools for Python, enabling data analysis and predictive modeling to uncover insights and trends.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill statsmodels -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 457 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as davila7/claude-code-templates.
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The statsmodels library enables AI agents to perform comprehensive statistical modeling and econometric analysis directly within Python workflows. It provides tools for hypothesis testing, time series analysis, regression diagnostics, and generalized linear models to derive insights from data.

When to use it

  • Validating assumptions and checking residuals in linear regression models to ensure reliability.
  • Analyzing trends and seasonality in time-series datasets like sales or stock prices.
  • Conducting hypothesis tests (e.g., t-tests, ANOVA) to determine statistical significance between groups.
  • Fitting generalized linear models (GLMs) for non-normal data distributions such as counts or binary outcomes.

Key capabilities

  • Linear and logistic regression analysis with built-in diagnostic tools.
  • Time series forecasting using ARIMA, SARIMA, and exponential smoothing methods.
  • Hypothesis testing suite including t-tests, chi-square, and F-tests.
  • Generalized linear models (GLM) for diverse data types.
  • Non-parametric tests like the Wilcoxon rank-sum test.

Example prompts

  • "Run a multiple linear regression on this dataset and output the p-values and R-squared score."
  • "Fit an ARIMA model to this time series and forecast the next 12 months of values."
  • "Perform a Shapiro-Wilk normality test on these residuals and interpret the result."

Tips & gotchas

Ensure your dataset is clean and properly formatted before running statistical tests, as outliers can skew results significantly. While statsmodels is powerful for inference, it lacks the automated feature engineering capabilities of machine learning libraries like scikit-learn, so manual data preprocessing may be required.