diff git a/lectures/lecture10/lecturenotes.tex b/lectures/lecture10/lecturenotes.tex
index 53019cf8f01bb919dad51920aa40626a9dd2002c..4152a7051b8938258a37380f60179d30a8f3d8f0 100644
 a/lectures/lecture10/lecturenotes.tex
+++ b/lectures/lecture10/lecturenotes.tex
@@ 25,9 +25,14 @@
of the statistics; eg. sample mean
\2 \textit{When might your samples not be independent of each other?}
\3 This is key because a lot of statistical tests require iid variables
+ \3 Throughput and latency
+ \3 Arrival times between events
+ \3 Two properties of an events (eg. read/write and latency)
\2 You can multiply together probs. when independent, have to start using
conditional probabilities when not
+ \3 Example: Sampling with replacement, sampling w/o replacement
\2 \textit{Why do we consider our measurements random variables?}
+ \3 They are affected by underlying random processes
\2 CDF vs. PDF vs. PMF
\2 \textit{How do we calculate probability a value will be within a range?}
\3 Integral (CDF) at point $b$, minus integral at point $a$
@@ 37,6 +42,7 @@
\2 Probability must be in range 0 to 1
\2 Independence
\2 Adding: mostly used for mutually exclusive events in the same trial
+ \3 eg. prob. of a write is prob. of insert plus prob. of update
\2 Multiplication: Used to calculate probability across multiple trials
\2 Sampling w/ replacement vs. w/o replacement: relationship to independence
@@ 44,10 +50,9 @@
\2 ``The value you can expect to get''
\2 AKA the mean
\2 PDF / PMF is balanced on the expected value
 \2 Variance (sigma squared) is the expected deviation from the mean
 (squared)
+ \2 Variance (sigma squared) is the expected deviation from the mean \\ (squared)
\2 $E[X]E[Y]$ is expected value of $X$ times expected value of $Y$
 \2 $E[XY]$ is expected value of $X * Y$
+ \2 $E[XY]$ is expected value of $X * Y$ (joint probability)
\2 Linearity of expectation
\1 Mean, median, mode
@@ 57,7 +62,7 @@
\2 Mistakes with means: large range, skewness, multiplying when not
independent, ratio with different bases
\1 Covariance
+\1 Covariance XXX
\2 Measure whether two random variables vary together
\2 Sign shows the tendency (together, or opposite)
\2 Joint probability distribution: eg. A given B
@@ 79,24 +84,19 @@
\2 Don't look at skewness
\2 Can only multiply means if independent
\2 \textit{When to use arithmetic vs. Geometric vs. harmonic mean}

\1 Means of ratios
 \2 Case 1: Sum of numerators and denominators both have physical meanings
 \3 eg. sum of CPU busy times over sum of experiment durations
 \2 Case 1a: Arithmetic mean can be used if bases are constant
 \2 Case 1b: Harmonic mean can be used if numerators are constant
 \2 Case 2: If cases are ``expected'' to be $a_i = cb_i$, can estimate
 $c$ by taking geometric mean

+ \3 Total is of interest (eg. time), product is of interest (eg.
+ speedup)
\1 Picking index of dispersion
\2 Range (when bounded)
+ \3 Use a variance based metric when using mean, using a percentile based
+ metric when using median
\2 Var or stddev (sttdev is in the right units)  see also CoV
\2 Percentiles  10 and 90, or 5 and 95 (want a sense of how long
things will take in extreme case
 \2 SIQR (semi interquanile range): middle 50% / 2: very outlierrobust
+ \2 SIQR (semi interquanile range): middle 50\% / 2: very outlierrobust
\2 Mean absolute dev (use least)
\1 Quantilequantile plots
+\1 Quantilequantile plots XXX
\2 For each quartile: plot pairs of what the theoretical distribution
should be, and what the empirical (sample) distribution actually is
\2 $x$axis: theoretical distribution
@@ 106,10 +106,8 @@
\2 Heavy tail / light tail
\1 For next time
 \2 HW \#5 due tonight
 \2 Reach Chapter 13 on comparing systems
 \2 HW \#6 posted
 \3 There is a part that you need to do \emph{before} class
+ \2 Read Chapter 13 on comparing systems
+ \2 HW \#5 due Friday