So let's have a look at some descriptive statistics. As I said in the introduction, it really is for me the first thing that I do when I start to analyze the data. When I look at all these columns and rows of data, I don't understand what it's trying to tell me, and I try to summarize it to descriptive statistics, and that is the first way that we try and find out what this data is trying to tell us. This hidden message, this hidden knowledge inside of the data. So there are many invoke functions that you might know about inside of Julia, and let's just run through the common ones. Mean is the first one. Remember, that's just where we take all of the numerical values in a list and we add all of them up and divide by how many there are, and when I call mean of age, we see that in this instance, yours is going to be different because we chose these numbers at pseudo-random, and that's 50.39, in my instance, median, just as simple, and 50 in my instance, remember, that's the value for which half are more than and half are less than. Median is what we would use if the data was very skewed. The standard deviation, that's the average difference between each of the values and the actual mean value. So once again, age was just a list of values, and we can run that cell, and we're going to get the standard deviation. Now remember, because some values are less than, and some are more than the mean, we have to actually square the differences between them, and that's where we get the variance from. The standard deviation is just the square root of the variance, and the function of these is called V-A-R, var. If, in this instance, you squared 17.67, you're going to get to about 312. Now, let's revisit the white cell count. Remember, because we chose a specific distribution. It was a normal distribution with a mean of 12 and a standard deviation of two. So let's just see, at random, that was just 100 values drawn at random. So it's not going to be precisely 12 and precisely 2, so let's see, on my instance, what the values were. So very close to the 12 that we selected for our distribution. We see 12.05 on my instance, and let's have a look at the standard deviation. There we go, 2.07. So 12.05 and 2.08 about, so very close to that distribution that we selected. Now, instead of doing all of these by hand, you can get some of them all built in into one function and that's the describe function. As I show you here, it comes from the stats base package. So again, you don't have to use the statsbase dot, I'm just putting them there so that you can see where this describe function comes from. So it statsbase dot described, and let's see what this is going to give us for the age. A nice summary statistics, or a summary statistics here. We see the mean of 50.39. We see the minimum was 20. We see the first quartile, the median, the third quartile, the maximum, and we see also length and type. We noticed that there were 100 values, and the datatype of these elements, remember there's going to be some inheritance up the type architecture at 64 bit integers. Now, statsbase has a second function that also does this for us, and that's called summary stats. Let's have a look at those summary stats. We see, everything is the same. We still get a median, first quartile median, two-column maximum, but it just drops that length and type, in case you don't want or need that information, so summary stat is going to do that for you. So really, the vast majority of the cases, this is the descriptive statistics that you're going to want. Right there, ready for you to write your report, or dissertation, or even paper. So really easy to get your descriptive statistics with Julia. Next stop, we're going to take all of the simulated data that we've created, and we're going to put that inside of a Data Frame.