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These are the user uploaded subtitles that are being translated: 1 00:00:05,180 --> 00:00:09,310 So a sales manager is back again and he's got some further questions for us. 2 00:00:09,320 --> 00:00:11,320 So I'm going to go back to my activity page. 3 00:00:11,330 --> 00:00:13,160 I've deleted the previous tables. 4 00:00:13,160 --> 00:00:15,020 We're just going to start with a blank page. 5 00:00:15,050 --> 00:00:20,270 So our first question is, is creates a new table displaying the sales by product name and they're going 6 00:00:20,270 --> 00:00:22,670 to sort the table from highest sales to lowest sales. 7 00:00:22,670 --> 00:00:25,310 We want to know which product has the highest sales. 8 00:00:25,970 --> 00:00:28,040 So we're going to use a table for this one. 9 00:00:28,250 --> 00:00:29,510 Just move it across. 10 00:00:30,400 --> 00:00:31,930 We want to see our product name. 11 00:00:32,080 --> 00:00:33,640 We want to see sales. 12 00:00:33,790 --> 00:00:35,610 Now we've got quite a lot of product names. 13 00:00:35,620 --> 00:00:43,200 Again, we could just use our sort so our highest and we can see that the Qantas projector 1080p x 90, 14 00:00:43,260 --> 00:00:46,150 well non ATO Black has the highest sales. 15 00:00:46,960 --> 00:00:50,260 We also need to know which sales is the lowest. 16 00:00:50,260 --> 00:00:55,660 So again, we can just change our sort order and we can see that that is a V USB data cable. 17 00:00:55,660 --> 00:00:57,040 E 600 gray. 18 00:00:57,560 --> 00:00:58,340 Okay, so there we go. 19 00:00:58,360 --> 00:01:00,220 Those are the first two questions. 20 00:01:00,520 --> 00:01:02,020 I'm going to actually delete this table. 21 00:01:02,020 --> 00:01:03,640 Let's just move on to a new one. 22 00:01:03,940 --> 00:01:06,830 Which product is sold in the most cities? 23 00:01:06,850 --> 00:01:09,040 Again, let's use a table for this. 24 00:01:09,870 --> 00:01:14,640 So in this case, we want to know the list of product names again, but we want to know the number of 25 00:01:14,640 --> 00:01:15,150 cities. 26 00:01:15,150 --> 00:01:18,540 So we want to know for each of the product names how many cities it's got. 27 00:01:18,810 --> 00:01:21,240 So we want to drag the cities and also drop it in there. 28 00:01:21,390 --> 00:01:26,100 So we'll get a text list of all the city names, but that's not what we actually want in this case. 29 00:01:26,100 --> 00:01:31,410 We want to know what is the distinct count, because you remember, we want to then count each time 30 00:01:31,410 --> 00:01:32,850 that a city is there. 31 00:01:32,910 --> 00:01:36,840 We want to know which product name is actually sold in the most cities. 32 00:01:36,960 --> 00:01:42,780 So in this case, when we do highest to lowest, it tells us that the lit way home theater system is 33 00:01:42,780 --> 00:01:44,580 actually sold in the most cities. 34 00:01:44,580 --> 00:01:46,560 So it sold in 22 cities. 35 00:01:47,580 --> 00:01:49,260 Okay, Let's move on to the next question. 36 00:01:49,800 --> 00:01:54,450 So for this question, we're going to be looking at a new table and we're going to show our sales by 37 00:01:54,450 --> 00:01:55,130 country. 38 00:01:55,140 --> 00:01:57,030 So again, let's just delete this one. 39 00:01:58,270 --> 00:01:59,440 The new table. 40 00:01:59,560 --> 00:02:02,290 And we just want our sales by country. 41 00:02:03,680 --> 00:02:06,410 Pop our country in their sales. 42 00:02:07,430 --> 00:02:12,710 And in this case, we want to know average which country has the highest average sales. 43 00:02:12,750 --> 00:02:13,120 Okay. 44 00:02:13,130 --> 00:02:13,490 Right. 45 00:02:13,490 --> 00:02:15,650 So we're going to need to change our method of aggregation. 46 00:02:15,650 --> 00:02:21,860 We're going to drop down, going to go to average, and we want to then change the sort order on this 47 00:02:22,130 --> 00:02:28,160 so we can see that China actually has the highest average cell at $4,518. 48 00:02:28,760 --> 00:02:32,480 The next one is which country has the lowest minimum sales, Right? 49 00:02:32,480 --> 00:02:36,230 So we need to change this again and we're going to go to a minimum. 50 00:02:36,320 --> 00:02:38,600 And in this case, though, we want to know the lowest. 51 00:02:39,190 --> 00:02:43,850 So we're just going to change the sort order again so we can see the China, Singapore and United States 52 00:02:43,850 --> 00:02:46,270 all have a value of five there. 53 00:02:46,310 --> 00:02:50,270 So basically all three countries has a $5 minimum. 54 00:02:50,960 --> 00:02:55,010 And we want to know which country has the highest maximum cell. 55 00:02:55,130 --> 00:03:00,260 So this again, change our method of aggregation back to maximum and just sort order. 56 00:03:01,340 --> 00:03:01,820 And then we go. 57 00:03:01,820 --> 00:03:08,180 We can see the United States had a sale of $78,312, which was the highest individual sale. 58 00:03:09,090 --> 00:03:09,870 Okay, moving on. 59 00:03:09,870 --> 00:03:11,760 We're going to create a new table. 60 00:03:12,060 --> 00:03:15,870 In this table, we're going to have the profit by city. 61 00:03:16,410 --> 00:03:18,330 So let's delete that again. 62 00:03:19,110 --> 00:03:20,220 Another table. 63 00:03:20,310 --> 00:03:21,540 Get our city in there. 64 00:03:21,990 --> 00:03:24,630 Let's get a profit it in there. 65 00:03:25,260 --> 00:03:26,850 Move this in a little bit. 66 00:03:27,270 --> 00:03:27,780 Right. 67 00:03:27,780 --> 00:03:29,400 So what's the questions we need to answer? 68 00:03:29,400 --> 00:03:33,120 We need to know which city has the highest number of transactions. 69 00:03:33,240 --> 00:03:37,140 So in this case, we're going to be using the count because we just need to know the count of the rows 70 00:03:37,140 --> 00:03:38,380 that are in the data set. 71 00:03:38,400 --> 00:03:40,140 So we're going to go into our count. 72 00:03:40,410 --> 00:03:42,450 This is going to give us the count of the rows. 73 00:03:42,450 --> 00:03:44,190 And again, we want to know the highest. 74 00:03:44,550 --> 00:03:50,340 So in this case, it's telling me that Beijing has 1398 rows that are in the data set. 75 00:03:50,340 --> 00:03:52,770 So there's the highest number of transactions. 76 00:03:53,280 --> 00:03:56,160 Next one is which city has the lowest average profit? 77 00:03:56,670 --> 00:03:57,810 So again, we're going to change this. 78 00:03:57,810 --> 00:03:59,820 So we want to now know what is the average. 79 00:04:00,330 --> 00:04:02,340 And we want to know what is the lowest. 80 00:04:03,350 --> 00:04:10,370 And we can see in this case it is Venetia has an average profit of $438, which is the lowest. 81 00:04:11,240 --> 00:04:16,640 The last question Which city sells the most different types of products, i.e. which city sells the 82 00:04:16,640 --> 00:04:18,769 most different types of product names? 83 00:04:18,980 --> 00:04:19,399 Right. 84 00:04:19,399 --> 00:04:22,280 So we're going to keep city, but we're going to be removing our profit for this one. 85 00:04:22,280 --> 00:04:28,040 So we want to bring our product name back in and we want to be doing a distinct count again on a product. 86 00:04:28,700 --> 00:04:33,650 So now for each city, it's telling me how many product names that city actually sells. 87 00:04:33,770 --> 00:04:35,570 And we want to know which ones got the most. 88 00:04:35,570 --> 00:04:41,300 So again, we can use our sort and you can see that Beijing actually sells the most types of product 89 00:04:41,300 --> 00:04:41,870 names. 90 00:04:41,870 --> 00:04:43,410 924. 91 00:04:43,430 --> 00:04:49,250 So what that's saying is basically that Beijing has sold 924 different types of product name. 92 00:04:50,050 --> 00:04:52,940 Okay, So hopefully our sales manager is going to be happy with those results. 93 00:04:52,960 --> 00:04:56,020 We're going to conclude the activity and I will see you in the next lesson. 8368

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