statsmodels

Fit OLS, GLM, ARIMA, and other statistical models in Python.

557|98|Updated Nov 7, 2025

Search Tags:#time series #hypothesis testing #statistics #regression #modeling #econometrics

Ready to run

npx skills add https://github.com/jimmc414/Kosmos --skill statsmodels-jimmc414

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/statsmodels
Command: npx skills add https://github.com/jimmc414/Kosmos --skill statsmodels-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive suite of tools for rigorous statistical analysis, hypothesis testing, and model diagnostics, enabling users to derive meaningful insights from data.

Core Features & Use Cases

Statistical Modeling: Fit OLS, GLM, logistic, time series (ARIMA), and more.
Hypothesis Testing: Perform t-tests, ANOVA, chi-square, and other statistical tests.
Model Diagnostics: Assess assumptions, detect outliers, and check for multicollinearity.
Use Case: Analyze the impact of advertising spend on sales using regression, test the statistical significance of the findings, and diagnose potential issues like heteroskedasticity.

Quick Start

Use the statsmodels skill to fit an OLS regression model with 'y' as the dependent variable and 'x1', 'x2' as independent variables.

Frequently Asked Questions about statsmodels

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform OLS regression and hypothesis testing on my dataset?▼

You can fit an OLS regression model by defining your dependent variable and independent variables, then apply hypothesis testing like t-tests to evaluate the statistical significance of your findings and relationships in the data.

What statistical models are available for time series analysis and econometrics?▼

For time series analysis and econometrics, you can fit ARIMA and VAR models alongside GLM, GLS, WLS, and discrete choice models to perform robust statistical inference and forecasting on your data.

Can I detect heteroskedasticity and outliers during model diagnostics?▼

Yes, model diagnostics support detecting outliers, identifying influence points, checking for multicollinearity, and performing residual analysis to assess modeling assumptions and heteroskedasticity issues.

What is the best way to test the statistical significance of advertising spend on sales?▼

The best way to test the impact of advertising spend on sales is using regression analysis to fit the relationship, followed by hypothesis testing and robust covariance estimation to validate statistical significance.

Does this statistical modeling toolkit require any external dependencies?▼

No external dependencies are required to use this statistical modeling toolkit, allowing you to directly perform regression, time series forecasting, and hypothesis testing without installing additional packages.