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Dispersion Analysis – Measures and Strategies to Follow

Dispersion analysis is a statistical technique that helps you find the sources of error and variability in your data. It can be used to identify outliers, determine whether your data is normally distributed, and determine whether sample estimates are consistent and reliable. Dispersion analysis is a term used in accounting to describe the difference between actual results and projected results. It is a measurement used by management as part of their financial statement reporting process, which helps them determine if they are on track with their goals or not.

In this article, we’ll cover some of the most common strategies for measuring dispersion and how to interpret them.

Dispersion Analysis:

A dispersion analysis is referred as a statistical method to detect the impact of the individual results on the outcomes of any research or experiment. This method is used to plan similar research subsequently. Fisher first gave this method in order to process the outcomes of agricultural experiments with the purpose of making the proper situations that can give maximum yield for a crop. Now, this analysis is used for many modern applications to resolve several issues in the fields of technology, biology, sociology and economics. This type of analysis is usually operated in terms of statistical theory to detect the difference between the outcomes of some direct measurements which are done under particular conditions.

For instance, take an example where a person wants to sell his house. You look for companies and narrow it down to two companies. Both companies guaranteed that the seller will get almost 95 percent of the price that they asked for. But, how can that person choose a company effectively?

The answer is very simple. You need to take an average to explain the data in a correct way to make a proper decision. However, taking an average cannot give that person all the details of the dataset as a whole. Mean does not tell what will happen in the set. Therefore, it is important to take a dispersion analysis because it can help that person make an informed decision on choosing a company that can give him the least risk and maximum benefit. Doing this analysis will help him to analyze each set. In this way, you can truly compare the two companies. Hiring a dissertation writing service UK is also helpful in this regard.

Measures and strategies:

In this section, we will focus on discussing the measures of dispersion and the methods to calculate them.


In dispersion analysis, you first calculate the range in the dataset. The range is usually the difference between the smallest and largest points in a data set. The main benefit of measuring the range of your data is that it is not very hard to calculate. However, it is also important to note that there are many drawbacks to range as well. The chances of outliers are very huge. The range also does not use all the points in the data. It just helps to give information about the maximum and minimum values in a data set.

The range is considered as the simplest measure in this analysis. It is widely used in statistics and very useful as it gives the largest and smaller numbers. In many cases, one will want to know the largest and smallest value. You can write the relative and absolute measure of range could calculate by using the following formulas:

In this, R is considered as the range, S is the smallest value, and L is the largest value.

Interquartile range:

Another measure for dispersion analysis is the interquartile range. This is referred to as the difference between the 75th percentile and 25th percentile (also known as the third and first quartile). This type of measure of dispersion explains the fifty percent of observations in the centre. If the value of the interquartile is large, then it shows that the middle fifty percent in the observations are separated with a wide space. The main benefit of this dispersion type is that it can be very useful for measuring the variability if you have not properly recorded the extreme values in the dataset. Another beneficial characteristic of interquartile range is that the extreme values do not impact it in the data set. This also has some drawbacks. For instance, using this measure cannot change the value to do mathematical manipulation.

To calculate the interquartile range to measure dispersion, you need to find the difference between the lower quartile and upper quartile. It usually follows the following formulas:

Q1 is the lower quartile and Q3 is the upper quartile.

Standard deviation:

Another measure that is used in the dispersion analysis is called standard deviation. It is the most commonly used method to measure dispersion. Standard deviation is referred as the spread of the dataset about the average. Standard deviation is considered as the square root of the sum of the squared deviation from average divided by the number of observations.

The above formula is used to calculate the standard deviation in an easy way. This formula does not give you rounding mistakes while calculating the dispersion. For advance calculation, you can use the formula below for standard deviation:

In the above formulas, you will use n-1 in the place of n. It is because n-1 can give you more accurate results. The benefit of using standard deviation is that it can help to avoid skewness. The drawback of calculating the standard deviation is that it does not appropriately measure dispersion with the skewed dataset.

Coefficient of variation:

Coefficient of variation is another measure used in the dispersion analysis. This method was given by Karl Pearson. This is very commonly used to measure dispersion in two series. You can calculate this by using the formula below:

In this, C.V. is the coefficient of variance.


With the help of the above, you can now clearly understand dispersion analysis. All the above measures will be enough for you to calculate your dataset’s dispersion. You must use the above formulas accurately to measure the data dispersion perfectly.

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