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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:04,580 --> 00:00:07,800 In the earlier lessons, you learned about measures of 2 00:00:07,799 --> 00:00:10,484 central tendency and measures of spread. 3 00:00:10,484 --> 00:00:12,000 As a business analyst, 4 00:00:12,000 --> 00:00:15,390 you should make sure to apply these statistical tools as 5 00:00:15,390 --> 00:00:19,245 you generate and examine the data using business metrics. 6 00:00:19,245 --> 00:00:22,620 A business metric is one data point that in 7 00:00:22,620 --> 00:00:26,355 itself does not tell much about the larger context. 8 00:00:26,355 --> 00:00:28,394 Much like other things in life, 9 00:00:28,394 --> 00:00:33,539 data makes more sense and has more value if it is looked at within a context. 10 00:00:33,539 --> 00:00:35,549 So, as a business analyst, 11 00:00:35,549 --> 00:00:38,489 your data analysis process should include 12 00:00:38,490 --> 00:00:42,020 exploratory checks to examine the spread of the data. 13 00:00:42,020 --> 00:00:47,875 You should always be asking and checking to see if the data is spread out equally in 14 00:00:47,875 --> 00:00:51,100 each direction and to see if the shape of 15 00:00:51,100 --> 00:00:55,314 the distribution resembles a normal distribution or not. 16 00:00:55,314 --> 00:00:58,989 This is important, because you need to know if 17 00:00:58,990 --> 00:01:03,020 what you're seeing is expected or out of the ordinary. 18 00:01:03,020 --> 00:01:04,844 In the previous lesson, 19 00:01:04,844 --> 00:01:09,025 we talked about skewness and we come back to it in this lesson. 20 00:01:09,025 --> 00:01:12,160 The box plot or histogram as you may 21 00:01:12,159 --> 00:01:16,134 remember are useful tools that tell you about skewness. 22 00:01:16,135 --> 00:01:19,525 They can alert you to any skewness in the data. 23 00:01:19,525 --> 00:01:23,455 Another way to check for skewness is to compare the mean 24 00:01:23,454 --> 00:01:27,754 and median values to see if they are more or less the same. 25 00:01:27,754 --> 00:01:31,969 Creating visualizations to look at data distributions and computing 26 00:01:31,969 --> 00:01:35,030 multiple summary statistics like mean and 27 00:01:35,030 --> 00:01:38,840 median should be a reflexive habit of a business analyst. 28 00:01:38,840 --> 00:01:43,000 Let's look at an example to understand why this is important. 2367

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