1 00:00:00,900 --> 00:00:01,560 I everyone. 2 00:00:02,530 --> 00:00:04,810 Let us start with the basics of statistics. 3 00:00:06,120 --> 00:00:09,900 What are the things we must know when we are planning to do that analysis? 4 00:00:10,950 --> 00:00:16,710 The first thing is the different types of data, we must know this because different types of analysis 5 00:00:16,950 --> 00:00:23,460 run on different types of data, and when we see the data, we should be able to quickly identify its 6 00:00:23,460 --> 00:00:27,660 type and then figure out what type of analysis is to be done on it. 7 00:00:28,410 --> 00:00:31,800 Basically, there are two types of data, qualitative and quantitative. 8 00:00:33,220 --> 00:00:37,000 Qualitative is also called categorical data because it has categories. 9 00:00:38,620 --> 00:00:44,560 Within qualitative, we have nominal and ordinal data, and within quantitative, we have discrete and 10 00:00:44,560 --> 00:00:45,430 continuous data. 11 00:00:46,400 --> 00:00:48,770 Let us look at these tapes one by one. 12 00:00:50,420 --> 00:00:56,960 Here's a definition of qualitative data data which can be classified into two or more categories is 13 00:00:56,960 --> 00:00:58,880 qualitative or categorical data. 14 00:01:00,200 --> 00:01:03,750 Such data is for the classified into nominal and ordinal. 15 00:01:04,220 --> 00:01:08,600 This is the capability of putting these categories into some order. 16 00:01:10,750 --> 00:01:18,400 Nominal is where we cannot assign any order, for example, if we collect our student and one of the 17 00:01:18,400 --> 00:01:20,470 variable is students gender. 18 00:01:21,380 --> 00:01:28,130 This data is categorical as it has categories such as male or female, but we cannot order these. 19 00:01:28,250 --> 00:01:31,930 We cannot assign that one or rank to any particular gender. 20 00:01:33,620 --> 00:01:41,240 Similarly, the country to which a person belongs is also nominal, it only has a name, it has no other 21 00:01:41,240 --> 00:01:41,830 information. 22 00:01:42,650 --> 00:01:47,660 It may have meaning for you, but the data analysis software will reach all countries equally. 23 00:01:48,850 --> 00:01:55,570 On the other hand, ordinal data has names and order, that is, you can rank the categories as higher 24 00:01:55,570 --> 00:01:56,150 or lower. 25 00:01:56,530 --> 00:02:04,330 For example, when they were in service of a restaurant, the options such as not satisfied, satisfied 26 00:02:04,330 --> 00:02:09,010 and delighted, can be ordered while doing analysis. 27 00:02:09,010 --> 00:02:12,560 It will be better that you tell the order of preference of these values. 28 00:02:13,420 --> 00:02:17,500 Similarly, spiciness of food is another ordinal categorical data. 29 00:02:18,040 --> 00:02:21,200 It contains values such as less spicy Mildenhall. 30 00:02:21,580 --> 00:02:27,100 We know that hot is more spicy than mine, but we do not know how much amount. 31 00:02:27,640 --> 00:02:34,000 We know that mild is more spicy than less spicy, but we cannot quantify the amount of spice. 32 00:02:34,060 --> 00:02:40,930 More spiciness that much less than less spicy to these three are categorical variables and we can order 33 00:02:40,930 --> 00:02:47,170 them in the order of spiciness, but they do not have particular numerical values assigned to it, so 34 00:02:47,170 --> 00:02:49,150 we cannot quantify it. 35 00:02:50,720 --> 00:02:52,760 Now, let us look at quantitative data. 36 00:02:54,000 --> 00:02:56,340 Quantitative data can be measured numerically. 37 00:02:57,300 --> 00:02:59,730 That is, it has a numerical value. 38 00:03:00,840 --> 00:03:03,720 It has inherent order, which is the numerical value. 39 00:03:04,690 --> 00:03:10,490 There are two types of quantitative variables, one which can take discrete values. 40 00:03:10,840 --> 00:03:14,960 This will have only certain values and cannot have intermediate values. 41 00:03:15,280 --> 00:03:23,200 For example, if you toss a coin 10 times, the number of times you'll get ahead can be an integer value 42 00:03:23,200 --> 00:03:28,420 from zero to 10, but it cannot be one point five or two point one, etc.. 43 00:03:29,710 --> 00:03:35,420 Second, by this continuous variable, continuous variables can take any value in between. 44 00:03:35,440 --> 00:03:35,800 Also. 45 00:03:37,180 --> 00:03:45,160 For example, I taught student it can be any value, like 170 centimetres, 170 point five centimeters, 46 00:03:45,340 --> 00:03:52,570 120 point five, six centimeters, etc., It may have a range between the values will fall, but within 47 00:03:52,570 --> 00:03:54,670 that range, it can take up any value. 48 00:03:55,760 --> 00:03:57,600 So these are the different types of data. 49 00:03:58,520 --> 00:04:03,560 Remember these keywords which we used in this lecture as these will be used in the lectures to come 50 00:04:03,560 --> 00:04:03,950 also.