statsmodels

Estimate linear, generalized, time series, ANOVA, and survival models with statsmodels.

19|2|Updated Jan 31, 2026

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

Ready to run

npx skills add https://github.com/tondevrel/scientific-agent-skills --skill statsmodels-tondevrel

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill provides advanced statistical modeling capabilities beyond basic data analysis, enabling rigorous hypothesis testing and in-depth understanding of data relationships.

Core Features & Use Cases

Statistical Modeling: Estimate complex models like OLS, GLM, ARIMA, and survival models.
Hypothesis Testing: Perform detailed statistical tests (ANOVA, t-tests, etc.) with p-values and confidence intervals.
Use Case: Analyze experimental results to determine the statistical significance of different treatment effects on an outcome variable, generating comprehensive summary tables for reporting.

Quick Start

Use the statsmodels skill to fit an OLS 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 statistical modeling and hypothesis testing in Python?▼

Statistical modeling and hypothesis testing are performed using Python's statsmodels library to estimate linear regression, generalized linear models, and conduct inferential statistics. This Skill facilitates model estimation and generates comprehensive summary tables for reporting.

What is the best way to analyze time series data and estimate ARIMA models?▼

Time series analysis and ARIMA model estimation are handled through this Skill's advanced statistical modeling capabilities. It enables rigorous data relationship analysis and model diagnostics using Python's statsmodels library.

Can I use statsmodels to generate R-style statistical reporting and summary tables?▼

R-style statistical reporting and comprehensive summary tables are generated as outputs of the model estimation process. This Skill satisfies requirements for inferential statistics and detailed model diagnostics.

How do I run ANOVA and t-tests to determine the statistical significance of treatment effects?▼

ANOVA, t-tests, and other detailed statistical tests are executed to determine the statistical significance of different treatment effects on an outcome variable. The process yields p-values, confidence intervals, and comprehensive summary tables.

Does this Skill support survival analysis and generalized linear models for experimental results?▼

Survival analysis and generalized linear models (GLM) are supported for analyzing experimental results. This Skill enables the estimation of these complex models alongside OLS regression and time series analysis.

When do I need advanced statistical modeling instead of basic data analysis?▼

Advanced statistical modeling is required when you need to perform rigorous hypothesis testing, estimate complex models like ARIMA or GLM, and generate inferential statistics with confidence intervals beyond basic data analysis.