Regressiostrategian

Regressiostrategian is a conceptual framework for planning and conducting regression-based analyses with an emphasis on methodological rigor, transparency, and interpretability. The term is not widely standardized and tends to appear in theoretical or pedagogical discussions of statistical practice as a holistic approach to regression work rather than a specific algorithm.

Etymology and usage: The word blends roots related to regression and strategy, signaling a deliberate, strategy-oriented approach to modeling relationships. It is used to describe a disciplined workflow that integrates design principles with statistical modeling, rather than to denote a single method.

Core principles: Regessiostrategian emphasizes careful problem framing, data quality assessment, and the clear articulation of assumptions. It promotes pre-registration or explicit analysis plans, appropriate handling of missing data, thoughtful variable selection, and robust model specification. Diagnostics, robustness checks, and transparent reporting of limitations are central, as is a focus on reproducibility.

Methods and tools: The framework encompasses a spectrum of regression techniques, including linear, logistic, and count models, as well as regularization methods such as Lasso, Ridge, and Elastic Net. It advocates using model selection criteria (AIC, BIC), cross-validation, and resampling. When causal interpretation is a goal, it integrates design-based or statistical causal methods and conducts sensitivity analyses to assess the robustness of findings.

Applications and reception: Regressiostrategian is applied across disciplines such as economics, psychology, epidemiology, ecology, and data science to organize regression workflows, improve reproducibility, and balance predictive accuracy with interpretability. As a relatively new or niche term, it is often criticized for vagueness unless paired with explicit methods and documentation.

See also: Regression analysis, Statistical modeling, Experimental design, Reproducibility in research.