1 00:00:01,630 --> 00:00:09,460 The next concept that we are going to see is person percentiles, if you see is actually a measure of 2 00:00:09,460 --> 00:00:10,060 dispersion. 3 00:00:10,870 --> 00:00:17,320 Let me explain this using an example, percentile is a way to rank the data. 4 00:00:18,490 --> 00:00:25,550 Here you see the height of different people are being analyzed and there are 20 people in this group. 5 00:00:26,170 --> 00:00:32,380 Suppose if someone sees the 80th percentile is five feet, 11 inches, it means. 6 00:00:33,360 --> 00:00:42,660 Eighty percent of the people are below five feet and 11 inches, this person, this person side is five 7 00:00:42,660 --> 00:00:43,670 feet, 11 inches. 8 00:00:44,040 --> 00:00:49,470 If this person corresponds to the 80th percentile, 80 percent of the people are below this person. 9 00:00:49,470 --> 00:00:51,240 Right, in terms of height. 10 00:00:52,400 --> 00:00:52,770 Right. 11 00:00:53,030 --> 00:00:56,040 So, you see, percentile is the ranking mechanism. 12 00:00:56,660 --> 00:01:06,920 This concept is used in competitive exams like it said Gary Gmod cat, you know, to rank these students 13 00:01:06,920 --> 00:01:07,670 performance. 14 00:01:08,950 --> 00:01:17,740 We going to use the concept of percentile to understand dispersion and also to identify outliers, outliers 15 00:01:17,740 --> 00:01:19,840 will be covered in other.