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
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.