 So, I welcome you all once again to my channel, Explore Education, I am Dr. Rashmi Singh, Assistant Professor, Department of Education, S.S. Khanna Girls degree college, University of Allahabad. And nowadays, we are discussing the topics of educational statistics in which I have already covered the concept of the measures of central tendency and how to compute mean, median and mode for both the grouped and ungrouped data. Now, it is the term of measures of variability. Okay? And the lecture will be in bilingual mode and it must be useful for your conceptual clarification. So, what we are discussing is measures of variability, which learn shield to come up and how vary, how we are differing, how much is the difference, we are going to talk about it. So, what is the need to understand this? What is the need to study measures of variability? And a lot of people found out what is the difference between a speciality or what can be explained to us. In this lecture, we discussed the data about measure of central tendency. So, what is the need of measure of variability? And where the directions of central tendency are low. You may now understand the importance of variability. So, what was the measure of central tendency? Gives and average of a set of observations and data. What was given to us? If there was an akra or a prediction, it would give us an ausat, central tendency. However, the average cannot be a true representation of data because of variations in the distribution. Here, the page is stuck. The page is stuck? It is saying that it is not necessary that the ausat is performing the right performance. Why is it so? Because there can be variations in the environment. There can be violence. There can be violence. Therefore, the average doesn't perform the right performance of our ausat many times. However, the average cannot be a true representation of data. For example, let's look at two data sets. Data 1, data 2. Data 1 has an ink. 8, 2, 6, 4, 8, 2, 10, 5, 5, 10. So, n is equal to i of 10. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. If the total is 60, then how much does it mean? If we have to run from the total to the end number, then it is 6. We have got the second data set. B has an ink. 7, 7, 7, 6, 7, 5, 5, 6, 5, 5, 5. Here, n is equal to 10 and total is equal to 60 and mean is equal to 6. That is, if we don't know the distribution of these two data sets, then we will come to know that we both have an ausat which is 6. In this data set, the first data set is varying from 2 to 10. That is, it has gone from 2 to 10, but the ausat is still 6. And here, it is varying from 5 to 7. Either it is 5 or 6 or 7. Even then, your ausat or the average is 6. This means that the ausat can't be told many times that it is 6 ausat. This means that it will be 5 or 6 or 7. We don't know that it can be 2 or even 10. That is, the distribution of these scores cannot be told by the measure of central tendency. This is what it says. That the mean is same for data A and data B. Both are good for the ausat or the average for the ausat. But the data vary in terms of their deviation from the mean. But both the ausat and the ink are very much fluctuating. Here, if it is 6, then it is 5 or 7. That is, it is different from plus 1 to minus 1. And here, it is different from minus 4 to plus 4. Minus 4, that is, 2 plus 4, that is, 10. Here, it is only minus 1 plus 1. So, it is saying that we need to know the concept of variability. We need to know how much data is varying from the mean. It is also called dispersal in data. How much difference is there in the data. How much difference is there in the ink. It actually refers to the variations that exist within and amongst these scores obtained by this group. That is, if you get an ink from a samosa, then how much are they varying from one to the other. We get to know from the concept of variability or dispersal. So, what is dispersal? It is an important statistic. This is a chart of Mahatpakon Sankhi which helps us to know how far the sample population varies from the universe population. We have talked to you many times about the universe population and the sample population. The universe is everything that we want to focus on. And since it is not practically possible that anyone, whoever you have covered in your population can go and study, then we have to choose its sample. The basis of the whole quantitative study is that you will be able to generalize as much as you choose the sample. Generalization means that you will be able to tell that the rule applies to the entire population. But what happens? Our sampling does not work. Why does the error come? We get to know the difference but what does the difference actually exist? Or the difference between the sample population and the universe population is due to some error. So, we will study this again. What is the standard error of the mean? What is the sampling error? We will study it later. For now, we are trying to understand the concept of measles of variability. After the measles of central tendency, we need to understand the measure of variability. To know that the average is that much, but how much data is differing from the average? Is it more or less differing? How is the difference? For this, we need the measure of variability. Where did this come from? I am wrong. No problem. So, measles of central tendency, variability in data, measles of central tendency provide us incomplete picture of set of data. It gives insufficient base for the comparison of two or more sets of scores. If we get two sets of data, then we do not have a basis for the difference between the two. Thus, in addition to a measure of central tendency, we need an index of how these scores are scattered around the centre of the distribution. So, apart from the marks of central tendency, we also need to tell them that the average is that much, and the average is that much. So, we need to tell them how many distribution centres are on the other side of the centre, in what respect? A measure of central tendency is just a summary of scores, and a measure of dispersion is a summary of the spread of scores. That is, on the basis of the average, we need to tell them the difference between the two. In a simple figure, we can tell them how much time is there, and what is its spread, and what is its impact. Okay. So, according to Minium, King and Beer, in 2001, they said that the results of variability express quantitatively the extent to which the score in the distribution scattered around or clustered together. That means, they said that the scales of the boundary can quantitatively express the limit of the score scattered around or clustered together. Clustered together means around, like 5, 6, 7, or the scatter is more, like 2, 6, 5, 8, 10, like this. So, this tells us the results of variability. They describe the spread of an entire set of scores. They do not specify how far a particular score diverges from the centre of the group. They tell us the spread, but they do not tell us how much a particular score diverges from the centre of the group. They give us a large amount of variability. So, the important thing is that the results of central tendency provide an insufficient base in comparison to us. So, the variability in data or the results of variability is important. And what is its importance? How did it come? I did not know. So, what is its importance is used to test the extent to which an average representative represents a specific data. The use of this is that we get to know the limit, the extent where the the target is representing the limit of the target. Then, helps in identifying the nature and cause of variation. We get to know the reason for the variation helps in the comparison of this spread into or more sets of data with respect to their uniformity and consistency. We get to know that if we have more than two eyes then we can determine the spread, spread, how uniform it is and how consistent it is. Let me tell you one more thing. Facilitates the uses of other statistical techniques such as correlation, regression and so on. One more thing is going on in all of this. How did it happen? One more thing I wanted to tell you that the variability of the amount of data you have will be homogenous. Homogenous means everything. So, what will be the variability? What will be the profit? What will be the higher the variability and what will be your data? It will be heterogeneous. There will be a lot of spread and a lot of diversity. So, this is the concept. Now, we have to know the absolute and relative dispersion and why we want to tell you all this. Because you have to discuss the mean deviation and standard deviation. So, first we get to know what is the deviation, the variability. We go straight to the question. Before the question, we don't understand the concept. First, we need to understand the concept. Then, we need to understand the question. Then, we need to remember the formula. We need to remember what is the right calculation. Then, we will know. So, absolute and relative dispersion. So, we need to know the exact amount of the dispersion and the exact amount of the dispersion. So, in measuring dispersion it is imperative to know the amount of variation. We should know how much it is varying. It is called absolute measure and the degree of variation. It is a spelling mistake and I don't know what I did when I made a slide. So, it is imperative to know the amount of variation. So, it is imperative to know the absolute measure and the degree of variation. So, it is imperative to know the relative measure. So, in the former case, in the absolute measure, we measure the range. We measure the mean deviation. In the course, we ask the standard deviation questions and the standard deviation is the basis. After that, you use further statistical techniques for the standard deviation. So, this absolute dispersion is the same way that we actually know how much the data is varying. So, the range is the same. So, the range is the same. And in the later case, we consider the relative measure. We consider the relative sapekshma. So, in sapekshma, we will take out how much the percentage is different and how much the percentage is varying. So, in this case, we take out coefficient of range, coefficient of mean deviation and coefficient of variation. So, we will take out the range mean deviation, standard deviation and in the relative measure, we will take out the coefficient of this. Thus, there are two broad classes from which measure of dispersion or variability i.e. the two main broad differences between the two. The first is absolute measure and the second is relative measure. They are absolute measure of dispersion and relative measure of dispersion. Absolute dispersion usually refers to the standard deviation. We talked about the absolute dispersion is the main type. And this tells us a measure of variation from the mean. Which is your data. And what does relative dispersion tell us? It is the result of dividing the standard deviation by the mean. i.e. when Manak Vishnan runs away from the object, what will come out is relative dispersion. i.e. sapeksh Vishnan. And it may be presented as quotient or percentage. And this is also taken out in some kind of a measure. So, what you have to understand is that measure of central tendency is superior. Why is it superior? We don't know whether it is superior or not. Summary of score is known, but spread of score is not known. So, for that measure of variability is needed. We also get to know how the data is spread. If it is homogenous, if it is like this, then the amount of variability will be less. If it is heterogenous, then the amount of variability will be more. And this is the two main types. When we know the actual amount, or if we have to remove it, then it is absolute dispersion. Absolute dispersion is an example of range, mean deviation or standard deviation. Or relative dispersion is an example of coefficient, coefficient of range, coefficient of standard deviation and coefficient of mean deviation. So, in this way, I have discussed with you all the concept of the measure of variability. And in my further videos, I will discuss with you all how to compute mean deviation, range and standard deviation. Okay? So, I have completed this very topic. Thank you and don't forget to like and subscribe to my channel for more information. I have done it from my side.