 In this video, I'm going to show you how to create a more complex report or more complex Crest tab using some of the Boolean connectors. So again, we are looking at Spring 2018 report. So that's our base. I'm going to go ahead. I'm going to go ahead and go directly into Composer. You can build a report using Boolean connectors in that main screen. Personally, I don't like it. Now, when I talk about Boolean connectors, we're talking about and, or, and, not. I'm going to create a more complex search using Boolean connectors. In the Composer, I'm going to build some categories. I'm going to build my own definition or my own row. So I'm going to start with gender again, and I'm going to look at age. I want lifestyle. And I want demographics. It's going to be personal information as opposed to head of household, et cetera. If you look at the ages, they give you some individual ages. They also give you some ranges. The ranges will change. There are some narrow ranges. There are some broader ranges. I've selected 18 to 24 and 25 to 34. Now I want to find out men between the ages of 18 and 24 and men between the ages of 25 and 34. What kind of, or what brands of energy drink they consume. So what I'm going to do, I select my ranges and I click on add. Now what I can, I can build a report by using this and option. But what I am going to do is just say, do bulk join. So I want this and. What this does is this automatically joins all of these searches into and. So when you're looking at men between the ages of 18 and 24, men, 25 to 34, female, women, 18, 24, women, 25 to 34. And then I want to add these to my row. Now I'm going to go back to energy drinks and I'm going to put these in columns. And then I'm going to go ahead and run the report chart type, cross tab view. I actually don't want to see this row. So I can actually delete these after the fact, because I really just want the men 18 and 24. Now I can delete these to edit name. If I want to, same thing as how I can, I can edit these. So again, when we read these, let's start with five hour energy. It's actually going to monster. This looks like a pretty good one of all those that drink monster, that drink monster the most 12.8% of them are men between the ages of 18 and 24 of all the men between the ages of 18 and 24 11.6% of them drink monster. Now if we see here, it says 217, that's the index. You could actually say they are 117% more likely to drink it. You could also say that men between the ages of 18 and 24 are twice as likely than the average to drink monster. If we look down here under the 25 to 34, it says 329, it means they are three times likely than the average. And we would read this the same way of all those that drink monster, 28.6% of them are men between the ages of 25 and 34 of all those, all men between the ages of 25 and 34, 17.5% of them drink monster. And again, we look at the little arrows. This is an up arrow. In a previous report, when I looked just at men and women, women in general were not drinking as many energy drinks as men, but you'll see the arrow here. It's saying this is really kind of an upward trend. You'll see the numbers are significantly different. Are the percentages of all those that drink monster, 8.9% of them are women between the ages of 18 and 24. Of all those women between the ages of 18 and 24, 8.4% of them drink monster. Now the index is still 157, so they are 57% more likely. It is smaller than twice as likely or three times as likely, but they are still more likely to. If you look at the other numbers, the sample and the weighted, so the number of people, so the sample was only 81, but they do estimate with the weighted number that 1.1 million women between the ages of 18 and 24 drink monster. Now if you look at that compared to the men, this is, there were 118 men between the ages of 18 and 24 who took this survey and just, let's see, 1.65 million men between the ages of 18 and 24 are projected to drink monster. Again, if you see numbers with asterisks, like over here with red bull, that means the data is shaky. That means they have a smaller sample, the projected number is smaller, and so as they say down here, use those numbers with caution. If I want to save this when I am done with this report, if this is the finished report and this is what I want to look at, I can go ahead and export this. If you want to re-import it so you can run it again, you save it as an SPC. I'm going to save this as an SPC file.