In the earlier note, we have seen how to create different types of graphics to extract valuable information from a variable (univariate analysis using plots and charts). Moreover, in this note, again, we will learn (univariate analysis using analytical tools) how to use analytical tools to extract the information about the central tendency of the data.
Measures of Central Tendency
The dataset contains many variables, and every variable has many observations. However, it isn’t easy to handle each observation from a variable and dig out the information from every observation. So the easiest way to quantify or summarize the information hidden inside the data or from a particular variable.
For example, the dataset contains the height and weight of the population. So seeing each observation does not give any meaningful information. Nevertheless, if we summarize the average height or shortest and tallest person from the population, this information will be utilized to make decisions.
Another example, suppose the last year’s temperature (in degree centigrade) of the following two cities in May for 5 days are recorded as follows:
What type of clothing is needed to visit these two cities in May? So by summarizing these observations, we can conclude that Varanasi has hot weather and Srinagar has cold weather, so that dresses will be selected accordingly.
Naturally, the human tendency is to compile the information in terms of average. For example:
- The average marks in a subject in a class are 70%, which means few students may receive 90% and others may obtain 45%. However, most of the students are having 70%.
- A medicine controls the fever for 6 hours, which means it is not exactly 6 hours. It may be 5.5 hours or 7 hours, and so on.
So, the statistical concept refers to the average or the central tendency of the data, and we measure the central tendency of the data using the various measures. A few of them are listed below.
References
- Descriptive Statistic, By Prof. Shalabh, Dept. of Mathematics and Statistics, IIT Kanpur.
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