diff git a/lectures/lecture16/lecturenotes.tex b/lectures/lecture16/lecturenotes.tex
index 7eed5d26dbf0773371af375d78558e9c1b4fe311..8b44eb8b479c20f5d92ad8822427743685265455 100644
 a/lectures/lecture16/lecturenotes.tex
+++ b/lectures/lecture16/lecturenotes.tex
@@ 15,7 +15,7 @@
\begin{outline}
\1 Today: How well does your data fit a line?
 \2 More complicated regressions exist, of course, but we'll stick with this one for now
+ \2 Talk about linear in detail, look at some more complicated ones in R
\2 Eyeballing is just not rigorous enough
\1 Basic model: $y_i = b_0 + b_1x_i + e_i$
@@ 74,17 +74,6 @@
\3 For example: How sure are we that two slopes are actually different
\2 \textit{When would we want to show that the confidence interval for $b_1$ includes zero?}
\1 Confidence intervals for predictions
 \2 Confidence intervals tightest near middle of sample
 \2 If we go far out, our confidence is low, which makes intuitive sense
 \2 $s_e \big(\frac{1}{m} + \frac{1}{n} + \frac{(x_p  \overline{x}^2)}{\sum_{x^2}  n \overline{x}^2}\big)^\frac{1}{2}$
 \2 $s_e$ is sttdev of error
 \2 $m$ is how many predictions we are making
 \2 $p$ is value at which we are predicting ($x$)
 \2 $x_p  \overline{x}$ is capturing difference from center of sample
 \2 \textit{Why is it smaller for more $m$}?
 \3 Accounts for variance, assumption of normal distribution

\1 Residuals
\2 AKA error values
\2 We can expect several things from them if our assumptions about regressions are correct
@@ 94,6 +83,11 @@
\2 QQ plot of error distribution vs. normal ditribution
\2 Want the spread of stddev to be constant across range
+\1 Switch to R
+ \2 Show example of linear fitting (good fit)
+ \2 Show example of linear fitting (bad fit)
+ \2 Show example of polynomial fit (intercept and 3 coefficients)
+
\1 For next time
\2 I won't be here week after spring break
\2 papers3 due Tuesday of spring break week
@@ 102,7 +96,8 @@
\2 lab2 now due Friday after spring break
\3 I want some more from you now, so be sure to update your fork
\3 Mainly, I want to know how you will improve the graph you
 are reproducing
+ are reproducing, and to actually look a bit at the code you
+ find
\end{outline}