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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:04,785 Let's look at this table for sale leads provided across the 10 regions. 2 00:00:04,785 --> 00:00:06,929 We've also provided the average here. 3 00:00:06,929 --> 00:00:09,330 The chart depicts the same information with 4 00:00:09,330 --> 00:00:12,585 the average sale leads depicted in the dash line. 5 00:00:12,585 --> 00:00:17,670 Now if you simply looked at the average sale leads across the 10 sales regions, 6 00:00:17,670 --> 00:00:22,575 you would assume that your sales team got about 430 leads on average. 7 00:00:22,574 --> 00:00:25,244 However, that is not the full story. 8 00:00:25,245 --> 00:00:29,850 That is the reason why you examine the median and you find out that 9 00:00:29,850 --> 00:00:34,725 actually 50 percent of your regions sell below the average. 10 00:00:34,725 --> 00:00:38,000 The median here is depicted in this purple line here. 11 00:00:38,000 --> 00:00:40,755 If you are presenting to your company executives, 12 00:00:40,755 --> 00:00:43,995 that is not a convincing story as they would wonder, 13 00:00:43,994 --> 00:00:47,765 "Well, what is a difference between a 100 leads? 14 00:00:47,765 --> 00:00:51,200 That looks like a big difference between the average and the median." 15 00:00:51,200 --> 00:00:54,620 That's why it's always important to look at the distribution. 16 00:00:54,619 --> 00:00:58,464 You see when we crafted and compare the mean and the median, 17 00:00:58,465 --> 00:01:02,130 you see that the region 10 did exceptionally well. 18 00:01:02,130 --> 00:01:08,885 This type of negatively skewed data necessitates the use of measures of central tendency. 19 00:01:08,885 --> 00:01:11,719 Looking at your measure of central tendency mean, 20 00:01:11,719 --> 00:01:14,359 median are very important as you are missing 21 00:01:14,359 --> 00:01:18,260 vital inflammation if you skip this part in your analysis. 22 00:01:18,260 --> 00:01:23,375 In other words, what you're seeing in region 10 was what was pulling the mean up. 23 00:01:23,375 --> 00:01:26,704 This revelation can lead to further investigation. 24 00:01:26,704 --> 00:01:31,469 For example, why did region 10 have so many more leads than the other regions? 25 00:01:31,469 --> 00:01:32,894 What was unique about it? 26 00:01:32,894 --> 00:01:36,409 Is there an untapped market we don't know about or 27 00:01:36,409 --> 00:01:40,465 maybe the sales manager in charge of that region did well? You get the drift. 28 00:01:40,465 --> 00:01:45,829 Looking at the larger context and the distribution always gives you 29 00:01:45,829 --> 00:01:53,155 more information than you can simply by looking at one number such as the average. 30 00:01:53,155 --> 00:01:56,750 These are important pieces of information that you need to bring 31 00:01:56,750 --> 00:02:00,670 back to your company and decision makers as a business analyst. 2905

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