Update examples/plot_fpca.py

Co-authored-by: Carlos Ramos Carreño <[email protected]>

examples/plot_fpca.py

@@ -27,11 +27,11 @@
 #
 # FPCA is a dimensionality reduction method for functional data that aims to 
 # reduce the complexity of studying observations by finding a finite number of
-# principal components, which are the directions that capture the main modes 
-# of variation across the function (the most important directions in which the
-# curves vary). FPCA can be though of as a basis expansion, but what 
+# principal components. These components are the directions that capture the 
+# main modes of variation across the function (the directions in which the
+# curves vary the most). FPCA can be though of as a basis expansion, but what 
 # distinguishes FPCA is that among all basis expansions that use K components
-# for a fixed K, the FPC expansion explains most of the variation in X.
+# for a fixed K, the FPCA expansion explains most of the variation in X.
 #
 # For more information abour FPCA and its objectives, see 
 # :footcite:ts:`wang+chiou+muller_2016_fpca`.