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.
Harrell’s primary mission is to combat . He argues against common but flawed practices like: Using P-values to select variables (Stepwise regression). Dropping "insignificant" variables from a final model. 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 It bridges the gap between high-level theory and
It is dense. It assumes a solid foundation in statistics and familiarity with R (specifically the rms package). Regression Modeling Strategies: With Applicatio...
Extensive use of restricted cubic splines to let the data dictate the shape of relationships.