doc headers

lib/statistics/stderr/Stderr.Validation.Tests.cs

@@ -196,18 +196,9 @@ public class StderrValidationTests
         }
     }
 
-    // ── Tulip Structural Note ─────────────────────────────────────────────────
-    //
-    // Tulip `stderr` is NOT the standard error of linear regression.
+    // Note: Tulip `stderr` is NOT the standard error of linear regression.
     // Tulip formula: stddev(x, n) / sqrt(n) = standard error of the mean.
     // QuanTAlib Stderr: sqrt(SSR / (n-2)) = standard error of OLS regression.
     // These are different statistics — no cross-validation is possible.
     // QuanTAlib is validated against its own brute-force OLS reference above.
-
-    [Fact(Skip = "Tulip stderr = StdDev/sqrt(n) (SE of mean); QuanTAlib Stderr = sqrt(SSR/(n-2)) (SE of OLS regression). Different statistics — intentional divergence.")]
-    public void Stderr_Structural_Note_TulipFormulaDiffers()
-    {
-        // Intentionally empty: test is always skipped via [Fact(Skip=...)].
-        // The Skip message documents the formula incompatibility with Tulip.
-    }
 }

lib/statistics/stderr/Stderr.md

@@ -1,6 +1,22 @@
-````markdown
 # Stderr: Standard Error of Regression
 
+| Property         | Value                            |
+| ---------------- | -------------------------------- |
+| **Category**     | Statistic                        |
+| **Inputs**       | Source (close)                          |
+| **Parameters**   | `period`                      |
+| **Outputs**      | Single series (Stderr)                       |
+| **Output range** | Varies (see docs)                     |
+| **Warmup**       | `period` bars                          |
+
+### TL;DR
+
+- ````markdown
+- Parameterized by `period`.
+- Output range: Varies (see docs).
+- Requires `period` bars of warmup before first valid output (IsHot = true).
+- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
+
 > "How confident are you in your line of best fit?"
 
 Standard Error of Regression (also called the Standard Error of the Estimate) measures the average distance that the observed values fall from the regression line. It quantifies the typical size of the residuals, providing a direct measure of how well a linear regression model fits the data.