 So, let's do an analysis, analysis of our data. Often, you do this phenomenal thing where you throw your data into a statistical evaluation. So, you use statistics to interpret your data. And in fact, that's always the case. To figure out if your data are significant, the findings that you are showing are significant. You need to use statistics. So, that's something that you often will employ the use of in your analysis. The other thing, and the thing that we will more likely engage in, is graphing. You can graph your results to help you visualize the outcome or the conclusion that you are going to draw. I'm bringing in the graph because I think it's important for us to make sure that we know that on the x-axis, as opposed to the y-axis, the x-axis is where the independent variable goes. And this, I think, makes graphing like, oh, so much easier. Because it's prescribed when you have an independent variable. So, for example, the amount of protein in the diet, that is what is going to go on the x-axis. And then the body weight, that was our dependent variable, that's the thing that's going to change. And we're going to expect to see, I mean, what kind of, what would we see like our body weight would, I can't, we'd have to figure out how we're going to set this in here. But we might see some kind of a trend where as protein increased, our body weight increased, or whatever, as protein, whatever. I did not think this through to show you that, oh, but we could totally have those results be possible. The point is that then we can see, we have a visual on the relationship between the two different variables. So, graphical analysis is what we will do. Statistical analysis is what the scientists do. And someday wouldn't it be awesome to incorporate statistical analysis into Viya 1?