Regression Analysis Expert

Expert regression analysis guidance covering model selection, diagnostics, regularization, and causal inference across OLS, GLM, and ML techniques.

Platinum

v1.0.00 activationsData & AnalyticsTechnologyintermediate

SupaScore

85.1

Research Quality (15%)

8.6

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.7

Completeness (10%)

8.4

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.6

Best for

▸Model selection between OLS, Ridge, Lasso for high-dimensional prediction problems
▸Diagnosing heteroskedasticity and multicollinearity in linear regression models
▸Implementing regularization techniques to prevent overfitting in sparse datasets
▸Causal inference analysis using instrumental variables and difference-in-differences
▸GLM specification for count data using Poisson and Negative Binomial regression

What you'll get

●Structured diagnostic checklist with specific statistical tests (Breusch-Pagan, VIF scores) and interpretation thresholds
●Model comparison framework with cross-validation metrics, AIC/BIC values, and recommendation rationale
●Code implementation guide with Python/R snippets for model fitting, diagnostics, and result interpretation

Not designed for ↓

×Deep learning or neural network architectures
×Time series forecasting with complex seasonality patterns
×Unsupervised learning like clustering or dimensionality reduction
×Real-time streaming analytics or big data processing

Expects

Dataset description with outcome variable type, sample size, predictor count, and clear analytical objective (prediction vs explanation vs causal inference).

Returns

Step-by-step regression analysis plan with model recommendations, diagnostic procedures, interpretation guidelines, and implementation code snippets.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

regressionstatisticslinear-regressionlogistic-regressionolsregularizationlassoridgemodel-diagnosticscausal-inferenceglmdata-analysis

Research Foundation: 8 sources (5 books, 2 official docs, 1 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release via Pipeline v3

Prerequisites

Use these skills first for best results.

Exploratory Data AnalysisGold

Works well with

Bayesian Statistics ExpertPlatinum Causal Inference AnalystGold Experimental Design SpecialistGold Python Data AnalystGold Statistical Analysis AdvisorPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Time Series Forecasting ExpertPlatinum Supervised Learning EngineerGold Causal Graph Modeling ExpertGold

Common Workflows

Complete Statistical Analysis Pipeline

End-to-end workflow from initial data exploration through regression modeling to comprehensive statistical interpretation and reporting.

Exploratory Data Analysis→regression-analysis-expert→Statistical Analysis Advisor

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