diff --git a/lectures/lecture14/lecturenotes.tex b/lectures/lecture14/lecturenotes.tex
index 5024897d8f56552cda26737f149429b7fe9a9e0a..70eff5dca6ccce53e02007fe21e02427f37b8c61 100644
--- a/lectures/lecture14/lecturenotes.tex
+++ b/lectures/lecture14/lecturenotes.tex
@@ -57,6 +57,49 @@
\2 In the end, gives us $b_1 = \frac{s^2_{xy}}{s_x^2}$
\3 Correlation of $x$ and $y$ divided by variance of $x$
\3 $\frac{\sum{xy} - n \overline{x} \overline{y}}{\sum{x^2} - n(\overline{x})^2}$
+
+\1 SS*
+ \2 SSE = Sum of squared errors
+ \2 SST = total sum of squares (TSS): difference from mean
+ \2 SS0 = square $\overline{y}$ $n$ times
+ \2 SSY = square of all $y$, so SST = SSY - SS0
+ \2 SSR = Error explained by regression: SST - SSE
+
+\1 Point of above: we can talk about two sources that explain variance: sum of
+ squared difference from mean, and sum of errors
+ \2 $R^2 = \frac{SSR}{SST}$
+ \2 The ratio is the amount that was explained by the regression - close to 1 is good (1 is max possible)
+ \2 If the regression sucks, SSR will be close to 0
+
+\1 Remember, our error terms and $b$s are random variables
+ \2 We can calculate stddev, etc. on them
+ \2 Variance is $s_e^2 = \frac{SSE}{n-2}$ - MSE, mean squared error
+ \2 Confidence intervals, too
+ \2 \textit{What do confidence intervals tell us in this case?}
+ \3 A: Our confidence in how close to the true slope our estimate is
+ \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
+ \2 They will not show trends: \textit{why would this be a problem}
+ \3 Tells us that an assumption has been violated
+ \3 If not randomly distributed for different $x$, tells us there is a systematic error at high or low values - error and predictor not independent
+ \2 Q-Q plot of error distribution vs. normal ditribution
+ \2 Want the spread of stddev to be constant across range
+
\1 For next time
\end{outline}