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python_data_stack

Fit statistical models and run diagnostics with statsmodels for regression, time series, and econometric analysis.

Updated Jan 14, 2026
Search Tags:#time series#regression#econometrics#statsmodels#arima#statistical inference#logistic regression
One-click install
npx skills add https://github.com/jvsandhu/agentic-skills --skill python-data-stack-jvsandhu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python_data_stack
Source: https://github.com/jvsandhu/agentic-skills/tree/main/skills/python_data_stack
Command: npx skills add https://github.com/jvsandhu/agentic-skills --skill python-data-stack-jvsandhu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, pandas, numpy, scipy, matplotlib, scikit-learn, and includes references (resource) components.

What problem does it solve? Performing rigorous statistical inference in Python requires choosing the right model for each outcome type, validating assumptions, and producing publication-ready results, which is error-prone without structured guidance. ## Core Features & Use Cases - Regression Modeling: Fit OLS, WLS, GLS, quantile, and mixed-effects linear models with robust standard errors and full diagnostic output. - Discrete & GLM Models: Run logistic, probit, multinomial, Poisson, Negative Binomial, and zero-inflated models with odds ratios and marginal effects. - Time Series Analysis: Build ARIMA, SARIMAX, VAR, and state space models with stationarity tests, ACF/PACF identification, and forecast intervals. - Use Case: An analyst fitting a logistic regression on customer churn data can use this skill to estimate odds ratios, check model fit, compute marginal effects, and validate predictions with AUC scoring. ## Quick Start Use the python_data_stack skill to fit an OLS regression on my dataset and show the full summary with residual diagnostics.

Frequently Asked Questions about python_data_stack

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

FAQPage Schema
How do I run a linear regression in Python with statsmodels?▼

Use sm.OLS with sm.add_constant() on your predictors, then call .fit() and .summary() for full inference output. The summary includes coefficients, p-values, R-squared, and diagnostic statistics.

How to fit an ARIMA time series model in Python?▼

Use statsmodels.tsa.arima.model.ARIMA with an order tuple like (1,1,1). First test stationarity with the ADF test, difference if needed, identify p and q from ACF/PACF plots, then call .fit() and .forecast().

statsmodels vs scikit-learn for regression, which should I use?▼

statsmodels is better for statistical inference since it provides p-values, confidence intervals, and diagnostic tests. scikit-learn focuses on prediction accuracy and pipelines without detailed inferential statistics.

When should I use Negative Binomial instead of Poisson regression?▼

Use Negative Binomial when count data shows overdispersion, meaning variance exceeds the mean. Check the Pearson chi-square divided by residual degrees of freedom; values above roughly 1.5 indicate overdispersion.

Why does my logistic regression fail to converge in statsmodels?▼

Non-convergence usually comes from perfect separation, collinear predictors, or unscaled features with extreme ranges. Check for separation by examining predicted probabilities near 0 or 1, and standardize continuous predictors.

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