From 46d32028c22ff58349c478927c9abbe6145a984c Mon Sep 17 00:00:00 2001
From: Gavin Chan <gavincyi@gmail.com>
Date: Thu, 8 Dec 2022 17:11:41 +0000
Subject: [PATCH] docs: correct typo

---
 docs/source/statistical.md     | 4 +---
 docs/source/statistical/pca.md | 3 +--
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/docs/source/statistical.md b/docs/source/statistical.md
index 73546a2..d3ac7b0 100644
--- a/docs/source/statistical.md
+++ b/docs/source/statistical.md
@@ -4,11 +4,9 @@ The statistical approaches deduces the factor structures from the sample returns
 covariance matrix of the estimation universe. One of the common approaches is
 principal components analysis (PCA)
 
-
-
 ```{toctree}
 :caption: Approach
 :maxdepth: 2
 
 statistical/pca
-```
\ No newline at end of file
+```
diff --git a/docs/source/statistical/pca.md b/docs/source/statistical/pca.md
index 8e2d87b..011b991 100644
--- a/docs/source/statistical/pca.md
+++ b/docs/source/statistical/pca.md
@@ -2,7 +2,7 @@
 
 Assume the historical instrument returns of the estimation universe is
 represented by a T x N matrix R. With singular value decomposition (SDV),
-the covariance matrix $\hat{Q}$ is decomposited by its eigenvectors and
+the covariance matrix $\hat{Q}$ is decomposed by its eigenvectors and
 eigenvalues.
 
 $$
@@ -28,4 +28,3 @@ $$
 
 where $W$ is the weight matrix in regression, e.g. an identity matrix in ordinary
 weighted least-squares.
-