pub fn leverage(fit: &OlsFit) -> Array1<f64>Expand description
Leverage hᵢ (the diagonal of the hat matrix) for each observation.
This is already computed efficiently at fit time from the thin Q factor —
the full n × n hat matrix is never materialized — and simply surfaced here.
§Interpretation
Leverage measures how unusual an observation’s predictor values are,
independent of its response. High leverage is potential influence, not
influence itself: a high-leverage point that happens to sit on the fitted line
barely moves it. Combine leverage with residual size — that is exactly what
cooks_distance and dffits do.
Each hᵢ ∈ [0, 1] and Σ hᵢ = p, so the average leverage is p/n; the
common flags are multiples of that average (2p/n, 3p/n).