diff --git a/07_final_assignment/.RData b/07_final_assignment/.RData
index 7478cef..3c7c07c 100644
--- a/07_final_assignment/.RData
+++ b/07_final_assignment/.RData
Binary files differ
diff --git a/07_final_assignment/baboonSimulation.R b/07_final_assignment/baboonSimulation.R
index 5dd6d77..d010c0d 100644
--- a/07_final_assignment/baboonSimulation.R
+++ b/07_final_assignment/baboonSimulation.R
@@ -525,7 +525,7 @@
 
 mat1 = matrix(rexp(15*15, rate=.1), ncol=15, nrow=15)
 
-conditions <- c("GeneralAccuracy", "WordAccuracy", "NonwordAccuracy")
+conditions <- c("NumWordsLearned")
 
 for(c in conditions) {
   image.plot(x=sort(aseq),
@@ -558,6 +558,7 @@
 
 
 
+
 ### WordAccuracy
 m1.w = gam(WordAccuracy ~ s(alpha) + s(beta), data=data)
 summary(m1.w)
@@ -568,6 +569,9 @@
 compareML(m1.w, m2.w)
 #m1 preferred
 
+vis.gam(m1.w, plot.type="contour", color="topo", main="WordAccuracy")
+
+
 
 ### NonwordAccuracy
 m1.n = gam(NonwordAccuracy ~ s(alpha) + s(beta), data=data)
@@ -602,6 +606,10 @@
 #note: this plot shows a model which is not justified because m2 is preferred.
 #it still is interesting, though.
 
+#explains the rather strange gam fit
+hist(data$NumWordsLearned, main="Histogram of NumWordsLearned",
+     xlab="NumWordsLearned")
+
 
 ### model criticism
 qqnorm(m1.g$residuals)
diff --git a/07_final_assignment/plots/plot_accuracy.pdf b/07_final_assignment/plots/plot_accuracy.pdf
new file mode 100644
index 0000000..bdfd8c8
--- /dev/null
+++ b/07_final_assignment/plots/plot_accuracy.pdf
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diff --git a/07_final_assignment/plots/plot_numwords.pdf b/07_final_assignment/plots/plot_numwords.pdf
new file mode 100644
index 0000000..206e3c0
--- /dev/null
+++ b/07_final_assignment/plots/plot_numwords.pdf
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