Today I learned
Statistics
While the following from statistics is too fundamental to be interesting, I struggled (and failed) to fully grasp it a few years ago. Hopefully, it will stick this time! Anyway, before I forget:
 Standard deviation is the square root of variance.
 Variance is the average of squared sum of differences between mean and observed values.
 Standard deviation is a measure of how spread out observed values are.
 Normal or Gaussian distribution is simply a bell curve, something that is fairly common in the real world.
 For such a distribution, 68% of the values lie within 1 standard deviation of the mean, 95% within 2 and 99.7% within 3.
 Standard normal disribution: is a (special) normal distribution which has a mean of 0 and standard deviation of 1.
 The yaxis of such a graph is just numbers but the xaxis is called the Zscore.
 You can convert a normal distribution to a standard normal distribution by calculating the Zscore as: ((observerdvalue  mean) / standarddeviation).
 The area under the curve is 1.
 If you search online, you’ll find a table that, for a given Zscore, gives the area of the curve on the left of that score. That will answer things like, what’s the probability that a given value X is less than something.
Resources:
 https://www.mathsisfun.com/data/standardnormaldistribution.html
 https://sphweb.bumc.bu.edu/otlt/MPHModules/BS/BS704_Probability/BS704_Probability9.html
D2L

Extensive literature considers column vectors to be the default orientation of vectors, so does this book.