All language subtitles for 005 Numerical Variables - Frequency Distribution Table_en

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These are the user uploaded subtitles that are being translated: 1 00:00:00,630 --> 00:00:02,670 Narrator: Okay, excellent. 2 00:00:02,670 --> 00:00:04,530 We already know how to create graphs 3 00:00:04,530 --> 00:00:06,930 and tables for categorical variables. 4 00:00:06,930 --> 00:00:09,030 In this lesson, we are going to do the same 5 00:00:09,030 --> 00:00:11,880 for numerical variables and given that numerical data 6 00:00:11,880 --> 00:00:13,680 is the main focus of this course, 7 00:00:13,680 --> 00:00:16,079 we will spend a couple of lessons on this topic. 8 00:00:16,950 --> 00:00:19,590 Whenever we wanna plot data, it is best to first order it 9 00:00:19,590 --> 00:00:23,400 in a table, so as we did with categorical variables, 10 00:00:23,400 --> 00:00:26,581 let's start by creating a frequency distribution table. 11 00:00:26,581 --> 00:00:29,022 Here's a list of 20 different numbers. 12 00:00:29,022 --> 00:00:31,440 If we arranged them in a frequency table 13 00:00:31,440 --> 00:00:33,780 like the one we use for categorical variables, 14 00:00:33,780 --> 00:00:36,090 we would obtain a table with 20 rows, 15 00:00:36,090 --> 00:00:37,800 each of them representing one number 16 00:00:37,800 --> 00:00:39,990 with a corresponding frequency of one 17 00:00:39,990 --> 00:00:43,230 as each number occurs exactly one time. 18 00:00:43,230 --> 00:00:47,024 This table would be impractical for any analysis, right? 19 00:00:47,024 --> 00:00:49,710 Well, when we deal with numerical variables, 20 00:00:49,710 --> 00:00:52,620 it makes much more sense to group the data into intervals 21 00:00:52,620 --> 00:00:55,050 and then find the corresponding frequencies. 22 00:00:55,050 --> 00:00:58,110 In this way, we make a summary of the data that allows 23 00:00:58,110 --> 00:01:00,303 for a meaningful visual representation. 24 00:01:01,350 --> 00:01:03,750 How do we choose these intervals? 25 00:01:03,750 --> 00:01:07,020 Generally, statisticians prefer working with groups of data 26 00:01:07,020 --> 00:01:09,870 that contain five to 20 intervals. 27 00:01:09,870 --> 00:01:12,360 This way, the summary can be useful. 28 00:01:12,360 --> 00:01:14,550 However, this varies from case to case 29 00:01:14,550 --> 00:01:16,770 and the correct choice of intervals largely depends 30 00:01:16,770 --> 00:01:19,560 on the amount of data we are working with. 31 00:01:19,560 --> 00:01:21,870 In our example, we will divide the data 32 00:01:21,870 --> 00:01:24,033 into five intervals of equal length. 33 00:01:25,140 --> 00:01:28,110 A simple formula that we use is as follows. 34 00:01:28,110 --> 00:01:30,660 The interval width is equal to the largest number 35 00:01:30,660 --> 00:01:32,970 minus the smallest number divided 36 00:01:32,970 --> 00:01:34,893 by the number of desired intervals. 37 00:01:36,030 --> 00:01:37,650 In our case, the length of the interval 38 00:01:37,650 --> 00:01:41,340 should be 100 minus one divided by five. 39 00:01:41,340 --> 00:01:43,383 The result is 19.8. 40 00:01:44,790 --> 00:01:46,950 Now we wanna round this number up 41 00:01:46,950 --> 00:01:49,980 in order to reach a neater representation. 42 00:01:49,980 --> 00:01:54,980 Therefore, our intervals will be as follows, one to 21, 43 00:01:55,170 --> 00:02:00,170 21 to 41, 41 to 61, 61 to 81, and 81 to 101. 44 00:02:02,760 --> 00:02:05,253 Each interval has a width of 20. 45 00:02:06,750 --> 00:02:07,800 Okay. 46 00:02:07,800 --> 00:02:10,953 Let's try to construct the frequency distribution table. 47 00:02:12,060 --> 00:02:14,610 A number is included in a particular interval 48 00:02:14,610 --> 00:02:17,100 if that number is greater than the lowest bound 49 00:02:17,100 --> 00:02:19,503 and equal to or less than the largest bound. 50 00:02:20,460 --> 00:02:21,990 As we can see from the table, 51 00:02:21,990 --> 00:02:23,811 there are two numbers in the first interval, 52 00:02:23,811 --> 00:02:27,480 four in the second, three in the third, six in the fourth, 53 00:02:27,480 --> 00:02:29,073 and five in the fifth interval. 54 00:02:30,570 --> 00:02:32,910 For many analyses, it is useful to calculate 55 00:02:32,910 --> 00:02:36,480 the relative frequency of the data points in each interval. 56 00:02:36,480 --> 00:02:38,340 As we said in a previous video, 57 00:02:38,340 --> 00:02:40,320 the relative frequency is the frequency 58 00:02:40,320 --> 00:02:42,453 of a given interval as part of the total. 59 00:02:43,470 --> 00:02:45,510 Let's add another column to our table 60 00:02:45,510 --> 00:02:47,850 and name it relative frequency. 61 00:02:47,850 --> 00:02:51,690 So the interval from one to 21 has an absolute frequency 62 00:02:51,690 --> 00:02:54,780 of two, but a relative frequency of two divided 63 00:02:54,780 --> 00:02:58,470 by the number of 20 numbers which gives us 10% 64 00:02:58,470 --> 00:03:00,573 and so on until we fill the table. 65 00:03:02,100 --> 00:03:02,940 All right! 66 00:03:02,940 --> 00:03:06,000 This is how we calculate relative frequencies. 67 00:03:06,000 --> 00:03:08,310 Now that we have summarized the raw data, 68 00:03:08,310 --> 00:03:09,573 we can start plotting it. 5428

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