Skills statsmodels

📦

statsmodels

Name: statsmodels
Author: K-Dense-AI

Content revision r1 Low Risk ⚡ Contains scripts⚙️ External commands🌐 Network access

Apply Statsmodels for Statistical Modeling

Statistical modeling often requires careful model choice, diagnostics, and interpretation. This skill guides Claude, Codex, and Claude Code through statsmodels workflows for rigorous Python analysis.

Supports: Claude Codex Code(CC)

📊 72 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "statsmodels" from https://skillstore.io/skills/k-dense-ai-statsmodels.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-statsmodels/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-statsmodels.md Signed manifest GET /api/skills/k-dense-ai-statsmodels/manifest Signed lockfile GET /api/skills/k-dense-ai-statsmodels/lockfile

Test it

Using "statsmodels". Fit an OLS model and explain whether the inference is reliable.

Expected outcome:

The response identifies the dependent and independent variables.
It recommends adding an intercept and fitting OLS.
It explains R-squared, coefficient signs, p-values, and confidence intervals.
It recommends heteroskedasticity, residual normality, and influence checks before final reporting.

Using "statsmodels". Analyze a binary outcome with statsmodels.

Expected outcome:

The response recommends Logit or Probit based on interpretation needs.
It describes odds ratios, marginal effects, predicted probabilities, and AUC.
It warns about convergence, separation, and threshold selection.

Using "statsmodels". Create a time series forecasting plan.

Expected outcome:

The response starts with plots and stationarity tests.
It uses ACF, PACF, and information criteria for model selection.
It includes residual diagnostics, forecast intervals, and out-of-sample validation.

Security Audit

Low Risk

v8 • 7/9/2026 Open versioned report

All static findings are false positives caused by markdown code fences, Python statistical examples, official documentation links, and statistical terms such as HC2, residuals, and degrees of freedom. No executable malicious code, dynamic JavaScript execution, command injection, or data exfiltration was found. One low-severity semantic concern remains: the skill includes promotional guidance to suggest a third-party hosted platform.

Files scanned

4,105

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Low

SKILL.md:613-614

Promotional External Platform Guidance

SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This is not code execution, but it can steer users toward a third-party hosted service beyond the core statsmodels guidance.

The cited lines explicitly direct promotional suggestions for an external service. The instruction is bounded and optional, so the concern is marketplace disclosure and user steering rather than malware.

Risk Factors

⚡ Contains scripts (8)

EVALUATION_OUTPUT.json:51 EVALUATION_OUTPUT.json:54 references/discrete_choice.md:94-95 references/discrete_choice.md:328-329 references/discrete_choice.md:448-449 references/time_series.md:114 references/time_series.md:118 references/time_series.md:189

⚙️ External commands (26)

🌐 Network access (4)

SKILL.md:608 SKILL.md:609 SKILL.md:610 SKILL.md:611

Audited by: claude View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). statsmodels security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-statsmodels/audits/8

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-statsmodels-2026,
  author = {K-Dense-AI},
  title = {statsmodels security audit report (audit version 8)},
  institution = {Skillstore},
  year = {2026},
  number = {8},
  url = {https://skillstore.io/skills/k-dense-ai-statsmodels/audits/8},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "statsmodels security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/k-dense-ai-statsmodels/audits/8"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-statsmodels:audit:8"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-statsmodels

Skillstore Score 75

Evidence Confidence High

Skillstore usage 7

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-statsmodels

Skillstore Score 72

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Build regression models

Fit linear models, check assumptions, choose robust standard errors, and interpret coefficient tables.

Analyze econometric questions

Compare model specifications, estimate marginal effects, and report inference with appropriate diagnostics.

Forecast time series

Check stationarity, select ARIMA or SARIMAX structures, evaluate residuals, and produce forecast intervals.

Try These Prompts

Fit a basic OLS model

Use statsmodels to fit an OLS model for my dataset. Add an intercept, summarize coefficients, and explain the key diagnostics.

Choose a GLM family

My outcome is not normally distributed. Help me choose a statsmodels GLM family and link function, then list diagnostics I should run.

Compare discrete choice models

Compare appropriate statsmodels models for this binary, multinomial, or count outcome. Include interpretation guidance and evaluation metrics.

Design a forecasting workflow

Design a statsmodels forecasting workflow for this time series. Include stationarity tests, model order selection, residual diagnostics, and validation.

Best Practices

Match the model family to the outcome type before fitting.
Run diagnostics after every fitted model and adjust inference when assumptions fail.
Report uncertainty, effect sizes, and validation results alongside point estimates.

Avoid

Do not use OLS for binary or count outcomes when GLM or discrete models fit better.
Do not interpret coefficients without accounting for link functions or scaling.
Do not rely on in-sample fit alone for forecasting or prediction claims.

Frequently Asked Questions

What is statsmodels best for?

Statsmodels is best for statistical inference, regression, diagnostics, econometrics, and time series models in Python.

How is statsmodels different from scikit-learn?

Statsmodels focuses on inference, coefficient tables, tests, and diagnostics. Scikit-learn focuses more on predictive machine learning workflows.

Can this skill choose the correct model automatically?

It can guide model choice, but final selection depends on the outcome, assumptions, data quality, and domain goals.

Does this skill run code by itself?

No. It provides documentation and guidance. Code execution depends on the host tool and user environment.

Which diagnostics should I run first?

Start with residual plots, heteroskedasticity checks, normality checks, autocorrelation tests, and influence diagnostics.

Can it help with time series forecasting?

Yes. It covers ARIMA, SARIMAX, VAR, stationarity testing, residual diagnostics, forecast intervals, and validation.

Developer Details

Author

K-Dense-AI

License

BSD-3-Clause license

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/statsmodels

Ref

1ffa7643651792ccb4bd3b15d924d2c97edff755

Maintenance freshness

7/18/2026

Usage

8 downloads · 543 views

File structure

📁 references/

📄 discrete_choice.md

📄 glm.md

📄 linear_models.md

📄 stats_diagnostics.md

📄 time_series.md

📄 EVALUATION_OUTPUT.json

Query Public Statistics with Data Commons

by davila7

Finding comparable public statistics across sources can require complex identifiers and API knowledge. This skill guides Data Commons queries, entity resolution, graph exploration, and result analysis.

Data & Analytics View

statsmodels

Install with my Agent

Agent-readable resources

Test it

Security Audit

Confirmed security concerns (1)

Risk Factors

Copy report link

Markdown badge

HTML badge

Embed card

APA citation

BibTeX citation

CITATION.cff

Compare variants

Skillstore Score

What You Can Build

Build regression models

Analyze econometric questions

Forecast time series

Try These Prompts

Best Practices

Avoid

Frequently Asked Questions

Developer Details

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