statsmodels

Name: statsmodels
Availability: InStock
Author: robinbarvaag

Fit OLS, GLM, logistic, and ARIMA models with statsmodels.

Updated Jan 10, 2026

Search Tags:#time series #statistics #regression #modeling #inference #econometrics

Ready to run

npx skills add https://github.com/robinbarvaag/poynt --skill statsmodels-robinbarvaag

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for statistical modeling, enabling rigorous inference and analysis across various domains.

Core Features & Use Cases

Statistical Modeling: Fit regression models (OLS, GLM, logistic), time series models (ARIMA), and perform hypothesis tests.
Data Analysis: Analyze complex datasets for statistical significance, trends, and patterns.
Use Case: Use this Skill to build a predictive model for customer churn based on historical data, including logistic regression and time series forecasting.

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 on a dataset?▼

To perform statistical modeling and hypothesis testing, you can use this toolkit to estimate linear models, generalized linear models, and run advanced diagnostics using R-style formulas for rigorous inference.

Can I use R-style formulas to fit an OLS regression model?▼

Yes, you can fit an OLS regression model using R-style formulas by specifying your dependent and independent variables directly to estimate parameters and generate diagnostic tests.

What is the best way to forecast time series data with ARIMA models?▼

The best way to forecast time series data is by fitting ARIMA models, which enable you to capture temporal trends, perform statistical inference, and generate predictive forecasts for historical data.

Does this toolkit support econometric modeling and discrete choice models?▼

Yes, this toolkit supports econometric modeling by enabling the estimation of discrete choice models, generalized linear models, and running advanced statistical tests for data analysis.

How do I build a logistic regression model to predict customer churn?▼

You can build a predictive model for customer churn by applying logistic regression to your historical data, estimating the model parameters, and evaluating statistical significance through hypothesis testing.