typos

notebooks/05_risk_decomposition_via_PCA.ipynb

@@ -19,7 +19,7 @@
     "1. To compute the sample covariance matrix $\\Sigma = \\frac{1}{T-1}X_c^T X_c$.\n",
     "2. To use PCA on $\\Sigma$ using `sklearn.decomposition.PCA` (which is really an eigendecomposition $\\Sigma = V\\Lambda V^T$ under the hood).\n",
     "3. To apply the Marchenko-Pastur cutoff from random matrix theory to separate signal eigenvalues from noise.\n",
-    "4. To implement Ledoit-Wolf shrinkage (a convex combination of sample and stuctured covariance) and show it improves out-of-sample estimation.\n",
+    "4. To implement Ledoit-Wolf shrinkage (a convex combination of sample and structured covariance) and show it improves out-of-sample estimation.\n",
     "5. To decompose the portfolio's variance $\\langle \\Sigma w, w \\rangle$ into the factor subspace and its orthogonal complement. \n",
     "\n",
     "### Terms used in this notebook\n",
@@ -45,7 +45,7 @@
     "## Notebook Structure\n",
     "1. [Setup and Imports](#setup-and-imports)\n",
     "2. [Load Data](#load-data)\n",
-    "3. [Sample Covariance and PCA from Scratch](#sample-covariance-and-pca-from-scratch)\n",
+    "3. [Sample Covariance and PCA](#sample-covariance-and-pca)\n",
     "4. [Marchenko–Pastur Noise Separation](#marchenkopastur-noise-separation)\n",
     "5. [Ledoit–Wolf Shrinkage](#ledoitwolf-shrinkage)\n",
     "6. [Portfolio Variance Decomposition](#portfolio-variance-decomposition)\n",
@@ -57,7 +57,7 @@
    "id": "08545070",
    "metadata": {},
    "source": [
-    "## Setups and Imports"
+    "## Setup and Imports"
    ]
   },
   {