00:00Alright, from your dataset, let's find out the good actors and the bad actors in terms
00:07of three most popular shoes sold and three least popular shoes sold.
00:12And to complete the statistics, we'll find the three most expensive shoes in store.
00:17So starting off with three most popular shoes sold.
00:19This is the formula that you need to use.
00:24The first argument on the filter is the whole dataset.
00:27The red ones are the quantity sold.
00:31And I'm using the function called large.
00:33Number five here pertains to column E minus one means descending order.
00:38Let's hit enter.
00:39And there you go.
00:40This is the most popular shoe sold on the store.
00:44Let's do it the top three least popular shoe.
00:47And this is the formula that you use.
00:50As you can see, I'm using a function called small here so that I can get the least popular.
00:57And number five means column five as in E and I'm using positive one here to indicate ascending
01:04order.
01:05So I'm going to hit enter here and you can see this is the least shoe sold on my dataset.
01:11And to top it off, we'll do the top three most expensive shoe in store.
01:15And there you have it, the three tables here.
01:21So let's do a quick analysis on what this table does from this three simple formula.
01:26You can see that the most expensive shoe, this one here, is one of the least sold shoe in
01:33my table here.
01:34You can see mainly because maybe the price is too high.
01:38So management might want to revisit the price point for this product here.
01:43And from this first table here on top here, you can see that it shows that the price point
01:49of a hundred dollars here for a shoe could be an optimal price point for many clients.
01:54So it's something to take a note of.
01:55So it's something to take a note of.
01:56So it's something to take a note of.
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