Academic rigor
Training and research across leading universities in psychology, measurement, and quantitative methods.
Statistical Models & Predictive Frameworks
From first principles to production: interpretable models, clean assumptions, and decision-ready uncertainty.
Explore the ToolkitNational research
High-stakes analytics in credentialing and public-interest contexts—where validity and auditability matter.
Big tech & consulting
People analytics, product-adjacent data science, and leadership/selection analytics—built for operators.
Where theory meets computation
PrimeStata combines classical inference with modern ML so leaders get two things at once: why something works (structure, assumptions, effect sizes) and how well it works (generalization, error bands, lift).
Regression craft & variance partitioning
Anatomy of prediction
Linear/logistic models with careful feature design, transformations, and interaction terms to reflect theory.
- R² / adjusted R²; partial η² for contribution
- Standardization (z-scores, beta weights) for comparability
- Outlier & influence checks (Cook’s D, leverage)
Assumptions & diagnostics
Make assumptions visible and fixable; only then are p-values and intervals decision-grade.
- Linearity, independence, normality of residuals
- Homoscedasticity vs. heteroskedasticity (robust SEs)
- Multicollinearity (VIF), residual structure & misspecification
Moderation & mediation
Model mechanisms and boundary conditions, not just correlations.
- Interaction terms & simple slopes
- Indirect effects (bootstrapped CIs)
- Sequential/parallel mediation, moderated mediation
Core modeling frameworks
GLM family
Linear, logistic, Poisson/negative binomial; link-function logic with interpretable parameters and robust errors.
Multilevel / mixed effects
Random intercepts & slopes for clustered data (teams, sites, time); cross-level moderation & shrinkage estimates.
Time series
ARIMA/ETS with seasonality, intervention analysis, and state-space models for operations and finance signals.
Survival / hazard
Cox PH, parametric survival, and competing risks for churn, retention, and time-to-event strategy.
Dimensionality
PCA/FA for structure finding; regularization (ridge/LASSO/elastic net) for parsimonious, stable predictors.
Bayesian inference
Hierarchical priors for partial pooling, posterior predictive checks, and decision-ready uncertainty summaries.
Data hygiene, uncertainty & robustness
Missingness
MCAR/MAR assessments, multiple imputation, sensitivity analyses, and transparent exclusions.
Validation
Holdout/CV, temporal splits, leakage prevention, and calibration curves for probability models.
Uncertainty
Effect sizes with CIs, prediction intervals, bootstrap stability, and practical significance thresholds.
Methods in practice
People & org analytics
Selection models, leadership/assessment utility, engagement drivers; adverse-impact audits with robust SEs.
Product & operations
Demand & capacity forecasting, quality/cycle-time models, uplift experiments, and risk-aware rollouts.
Finance & growth
Portfolio tilts, event studies, pricing elasticity, and cohort LTV models with survival and hazards.
Artifacts & deliverables
Model brief
Plain-language summary, assumptions, key effects, and “how to use” guidance for decision-makers.
Tech appendix
Specs, diagnostics, code-book, fit indices, variance decomposition, robustness checks, and reproducibility notes.
Operator-ready views
Role-based dashboards, error bands, scenario toggles, and action thresholds wired to KPIs.
Related flagship path
This modeling layer most directly supports Data Science work when leaders need forecasting, experimentation, or interpretable prediction tied to a concrete operational decision.
Partner on a modeling problem
Bring a dataset, a decision, or a hypothesis. We’ll map assumptions, choose an appropriate model, and ship interpretable results.