typos

doc/pub/DecisionTrees/html/._DecisionTrees-bs008.html

@@ -187,7 +187,7 @@ of features \( p \) is not too large.
 <p>
 Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
-further so as to minimize the RSS within each of the resulting
+further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
 space, we split one of the two previously identified regions. We now
 have three regions. Again, we look to split one of these three regions

doc/pub/DecisionTrees/html/DecisionTrees-reveal.html

@@ -522,7 +522,7 @@ of features \( p \) is not too large.
 <p>
 Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
-further so as to minimize the RSS within each of the resulting
+further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
 space, we split one of the two previously identified regions. We now
 have three regions. Again, we look to split one of these three regions

doc/pub/DecisionTrees/html/DecisionTrees-solarized.html

@@ -490,7 +490,7 @@ of features \( p \) is not too large.
 <p>
 Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
-further so as to minimize the RSS within each of the resulting
+further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
 space, we split one of the two previously identified regions. We now
 have three regions. Again, we look to split one of these three regions

doc/pub/DecisionTrees/html/DecisionTrees.html

@@ -495,7 +495,7 @@ of features \( p \) is not too large.
 <p>
 Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
-further so as to minimize the RSS within each of the resulting
+further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
 space, we split one of the two previously identified regions. We now
 have three regions. Again, we look to split one of these three regions

doc/src/DecisionTrees/DecisionTrees.do.txt

@@ -334,7 +334,7 @@ of features $p$ is not too large.
 
 Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
-further so as to minimize the RSS within each of the resulting
+further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
 space, we split one of the two previously identified regions. We now
 have three regions. Again, we look to split one of these three regions