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Descriptive statistics is a field of statistics that describes or explains a set of data. They are used for two purposes – to show potential relationships between variables and to provide an overall summary of variables in a data set. Initially, when we are given a set of data, we first try to understand it instead of just rushing ad applying fancy algorithms. Descriptive statistics help us summarize and organize data so that we can easily understand it.
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Descriptive analysis can be divided into two major categories:
Central tendency means that there is a number that in some way summarizes the entire data set. There are three main methods to show the central tendency. These include:
For example, let’s say you have five people whose weight is 60, 70, 80, 90, 100 kg. To get the mean, just add the numbers, and divide by 5. So, it will be 60+70+80+90+100=400. Then 400/5=80. In this case, the average weight will be 80 kg.
To use mean as a measure of central tendency your data must be numerical. You cannot use mean when your dataset it made of nominal data.
For example, consider a dataset with the height (in inches) of ten people: 56, 56, 56, 57, 57, 58, 59, 59, 60, 61. As you can see, 56 is the most common value, hence the mode of this set of data would be 56. The mode has one benefit over the mean and median – it can be used for both numerical and nominal datasets.
For example, the median of the following set of data would be 27 because it is the value at the center.
22, 23, 25, 25, 27, 28, 29, 32, 33
But this example has an odd set of values (9 numbers). What happens when you have an even dataset? Easy! Just locate the two numbers in the middle and divide them by two.
For example, the following data set has 10 values.
22, 23, 25, 25, 27, 28, 29, 32, 33, 34
The median will therefore be (27+28) then divide by 2, which will be 27.5
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While central tendency shows us important information about a dataset, it doesn’t really reveal everything we need to know about the dataset. For instance, it doesn’t show us the extent to which individual values differ in a dataset. According to our descriptive statistics assignment help experts, measures of dispersion give a lot more information about a dataset. They show us the averages of the values in a dataset and allow us to learn and interpret them more comprehensively. Dispersion describes how data values are spread within a given set of data. There are two common measures of dispersion in statistics; the range and standard deviation.
Suppose you have two sets of data, one with retirement age and the other with height (in inches) of a given population.
Data set 1 (Age): 57, 59, 61, 63, 65
Data set 2 (Height): 45, 55, 65, 75, 85
Here is how to calculate the range of both data sets:
Data set 1: 65 – 57 = 8
Data set 2: 85 – 45 = 40
As you can see, the values in data set 1 are closer to the mean than those in data set 2. The major downside of the range as a measure of dispersion is that it only gives us information about the maximum and minimum of the set of data. It doesn’t give any information about the values that are in between. To learn more about the range as a measure of dispersion, contact our descriptive statistics homework helpers.
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