{HS: The histogram overall shape resembles Gaussian (or normal) distribution, but it is a discrete distribution. Try to prove by counting, or finding convolutions of three uniform distributions, that the probability distribution is, starting from sum=3 event, 1/216, 1/72, 1/36, 5/108, 5/72, 7/72, 25/216, 1/8, and symmetrically going down to 1/216 for sum=18 event. If you use more dice (instead of three) it gets closer to the Gaussian (central limit theorem).}
{NTR: Terminology. The word 'not random', in the context of pre-lab 9, is better swapped with 'not uniform distribution'. The uniform distribution has the most entropy (randomness), but we don't call other distributions 'not random' or 'not totally/really random'.}
{CR: One check for the randomness from histogram is to find the exact solution and see if it matches the distribution found using 100 sample events. You can also calculate the uncertainties of each point, doing multiple trials or probability calculations. In general, testing randomness is an important and much more subtle problem. One might need to consider the higher statistics or correlations. We can discuss this if you are more interested.}
{IND: There are also measures to see if the events are independent. Here, it is rather clear that the consecutive events are independent from each other, because we consider each dice roll to have uniform 1/6 probabilities for each side. In general, you might need to check the independnece using data. We can discuss this if you are more interested.}
{V: Vague and unscientific use of language. For example, for question 3a in this pre-lab 9, you need to understand what 'random' mean.}