diff --git a/principal_component_analysis.ipynb b/principal_component_analysis.ipynb
index efbf762..f96314a 100644
--- a/principal_component_analysis.ipynb
+++ b/principal_component_analysis.ipynb
@@ -215,7 +215,7 @@
    "source": [
     "Often, the desired goal is to reduce the dimensions of a $d$-dimensional dataset by projecting it onto a $(k)$-dimensional subspace (where $k\\;<\\;d$) in order to increase the computational efficiency while retaining most of the information. An important question is \"what is the size of $k$ that represents the data 'well'?\"\n",
     "\n",
-    "Later, we will compute eigenvectors (the principal components) of a dataset and collect them in a projection matrix. Each of those eigenvectors is associated with an eigenvalue which can be interpreted as the \"length\" or \"magnitude\" of the corresponding eigenvector. If some eigenvalues have a significantly larger magnitude than others that the reduction of the dataset via PCA onto a smaller dimensional subspace by dropping the \"less informative\" eigenpairs is reasonable.\n"
+    "Later, we will compute eigenvectors (the principal components) of a dataset and collect them in a projection matrix. Each of those eigenvectors is associated with an eigenvalue which can be interpreted as the \"length\" or \"magnitude\" of the corresponding eigenvector. If some eigenvalues have a significantly larger magnitude than others then the reduction of the dataset via PCA onto a smaller dimensional subspace by dropping the \"less informative\" eigenpairs is reasonable.\n"
    ]
   },
   {