From d17a747a4ec06786c1d352c41b7d31de6513c1a2 Mon Sep 17 00:00:00 2001
From: "Josiah K. Kimani" <85480392+josiahkim29@users.noreply.github.com>
Date: Tue, 10 Dec 2024 10:50:56 +0200
Subject: [PATCH] Update 19_spat_stat.ipynb

typo in line 11 "capture"
---
 19_spat_stat.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/19_spat_stat.ipynb b/19_spat_stat.ipynb
index 5e61195..8917e91 100644
--- a/19_spat_stat.ipynb
+++ b/19_spat_stat.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "<font color='orange'>Spatial statistics</font>  is a branch of statistics that deals with the analysis and interpretation of data that has spatial or geographical components, considering how neighboring locations influence each other. It involves techniques for exploring, modelling, and understanding the patterns and relationships within spatial data. The state-of-the-art approach to spatial data analysis are hierarchical Bayesian models {cite}`banerjee2003hierarchical`.\n",
     "\n",
-    "Since Gaussian processes are able to cpature correlation at different locations depending on the disctance between those locations, it makes them a foudational tool used in spatial modelling. In GLMMs, the term presented by latent GPs, or their close relatives - multivariate Normal distribution - play the role of the <font color='orange'>spatial random effect</font>. "
+    "Since Gaussian processes are able to capture correlation at different locations depending on the disctance between those locations, it makes them a foudational tool used in spatial modelling. In GLMMs, the term presented by latent GPs, or their close relatives - multivariate Normal distribution - play the role of the <font color='orange'>spatial random effect</font>. "
    ]
   },
   {