Provides clear rules of thumb (like the 15-to-1 ratio) for how many variables a dataset can actually support. ⚖️ The Verdict
Heavy emphasis on multiple imputation rather than deleting rows.
Categorizing continuous predictors (e.g., splitting age into groups). 🛠️ Key Technical Strengths
A rigorous focus on bootstrapping for internal validation rather than simple data-splitting.
It bridges the gap between high-level theory and "boots-on-the-ground" data analysis. It teaches you how to build models that actually replicate in the real world.
Extensive use of restricted cubic splines to let the data dictate the shape of relationships.
Regression Modeling Strategies: With Applicatio... -
Provides clear rules of thumb (like the 15-to-1 ratio) for how many variables a dataset can actually support. ⚖️ The Verdict
Heavy emphasis on multiple imputation rather than deleting rows.
Categorizing continuous predictors (e.g., splitting age into groups). 🛠️ Key Technical Strengths
A rigorous focus on bootstrapping for internal validation rather than simple data-splitting.
It bridges the gap between high-level theory and "boots-on-the-ground" data analysis. It teaches you how to build models that actually replicate in the real world.
Extensive use of restricted cubic splines to let the data dictate the shape of relationships.
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