Explains the statistical concept of included-variable bias in regression models, challenging the traditional 'omitted-variable bias' framing.
Analyzes four datasets with high collinearity between predictors, demonstrating statistical diagnostics and modeling approaches using R.
Explores missing likelihood-ratio tests in survey regression models, comparing Wald, score, and Rao-Scott tests with sample vs. population scaling.
A guide to calculating marginal and conditional effects in generalized linear mixed models (GLMMs) using the R {marginaleffects} package.
A technical article explaining polynomial distributed lag models for regularization in time-series analysis, including code archaeology and R implementation.
A guide explaining marginal effects in regression analysis, including definitions and differences between types like average marginal effects, using R packages.
A tutorial on using R to diagnose biases in two-way fixed effects (TWFE) regression models when treatment timing varies, based on Pamela Jakiela's paper.
A tutorial using Euler/Venn diagrams to visualize and explain the R² statistic and variance in regression models.
A statistical analysis of multicollinearity in regression models, discussing its impact on coefficient interpretation and prediction.
Explores the statistical challenges and potential bias when adjusting stratification variables during multi-wave sampling for population estimation.
Explains methods like regression and inverse probability weighting to close confounding backdoors in DAGs for causal inference in observational data.
A two-day workshop on survival analysis, covering data exploration, regression modeling, and practical sessions for time-to-event data.
Explains linear splines, their mathematical basis, and two practical parametrizations for regression, comparing them to higher-degree splines.
A developer investigates and solves a complex iOS 8 UIKit regression involving unwanted screen rotation when using multiple custom windows.
Explores the concept of 'error' in regression models, clarifying when it represents measurement error versus model prediction error.
