In statistics, measures of dispersion quantify the spread or variability of a dataset. Range shows how much greater the highest value a data set. Variance computes the mean square deviation of all observations; standard deviation is the square root of variance. They are widely used because they can be sensitive towards outliers and also present a very simple interpretation.

Read on this article to learn more about measure of dispersion, its types, formulas, and examples.

## What is Dispersion in Statistics?

Dispersion in statistics is a way of describing how to spread out a set of data is. Dispersion is the state of data getting dispersed, stretched, or spread out in different categories. It involves finding the size of distribution values that are expected from the set of data for the specific variable. The meaning of dispersion in statistics is “numeric data that is likely to vary at any instance of average value assumption”.

Dispersion of data in Statistics helps one to easily understand the dataset by classifying them into their own specific dispersion criteria like variance, standard deviation and ranging.

Dispersion is a set of measures that helps one to determine the quality of data in an objectively quantifiable manner. Most often Data Science courses start with the basics of statistics and dispersion is one such concept that you cannot afford to skip.

## Measures of Dispersion

The measures of dispersion contain almost the same unit as the quantity being measured. There are many Measures of Dispersion found that help us to get more insights into the data:

- Range
- Variance
- Standard Deviation
- Skewness
- IQR
** **

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### Types of Measures of Dispersion

The Measure of Dispersion in Statistics is divided into two main categories and offer ways of measuring the diverse nature of data. It is mainly used in biological statistics. We can easily classify them by checking whether they contain units or not.

So as per the above, we can divide the data into two categories which are:

- Absolute Measures of Dispersion
- Relative Measures of Dispersion

**Category** | **Measure** | **Formula** |

**1. Absolute Measure of Dispersion** | Range | R = Maximum Value - Minimum Value |

| Mean (μ) | μ = Σ(x) / N |

| Variance (σ²) | σ² = Σ(x - μ)² / N |

| Standard Deviation (σ) | σ = √σ² |

| Quartile | - |

| Quartile Deviation (QD) | QD = Q3 - Q1 |

| Mean Deviation (MD) | * MD (Mean): Σ |

**2. Relative Measure of Dispersion** | Coefficient of Range (CR) | CR = (Maximum Value - Minimum Value) / (Maximum Value + Minimum Value) |

| Coefficient of Variation (CV) | CV = (σ / μ) * 100% |

| Coefficient of Standard Deviation | σ / μ |

| Coefficient of Quartile Deviation | (Q3 - Q1) / (Q3 + Q1) |

| Coefficient of Mean Deviation | Same as Mean Deviation (formula depends on Mean or Median) |

## 1. Absolute Measures of Dispersion

Absolute Measures of Dispersion is one with units; it has the same unit as the initial dataset. Absolute Measure of Dispersion is expressed in terms of the average of the dispersion quantities like Standard or Mean deviation. The Absolute Measure of Dispersion can be expressed in units such as Rupees, Centimeter, Marks, kilograms, and other quantities that are measured depending on the situation.

**Types of Absolute Measure of Dispersion in Statistics: ** ** **

**Range:** Range is the measure of the difference between the largest and smallest value of the data variability. The range is the simplest form of Measures of Dispersion.

- Example: 1,2,3,4,5,6,7
- Range = Highest value – Lowest value
- = ( 7 – 1 ) = 6

**Mean (μ):** Mean is calculated as the average of the numbers. To calculate the Mean, add all the outcomes and then divide it with the total number of terms.

Example: 1,2,3,4,5,6,7,8

- Mean = (sum of all the terms / total number of terms)

= (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) / 8

= 36 / 8

= 4.5

**Variance (σ2):** In simple terms, the variance can be calculated by obtaining the sum of the squared distance of each term in the distribution from the Mean, and then dividing this by the total number of the terms in the distribution.

It basically shows how far a number, for example, a student’s mark in an exam, is from the Mean of the entire class.

**Formula:**** **

(σ2) = ∑ ( X − μ)2 / N

**Standard Deviation:**** **Standard Deviation can be represented as the square root of Variance. To find the standard deviation of any data, you need to find the variance first. Standard Deviation is considered the best measure of dispersion.

**Formula:**** **

Standard Deviation = √σ

**Quartile:** Quartiles divide the list of numbers or data into quarters.

**Quartile Deviation****:** Quartile Deviation is the measure of the difference between the upper and lower quartile. This measure of deviation is also known as the interquartile range.

**Formula:**** **

Interquartile Range: Q3 – Q1.

**Mean deviation:** Mean Deviation is also known as an average deviation; it can be computed using the Mean or Median of the data. Mean deviation is represented as the arithmetic deviation of a different item that follows the central tendency.

**Formula:**** **

As mentioned, the Mean Deviation can be calculated using Mean and Median.

- Mean Deviation using Mean: ∑ | X – M | / N
- Mean Deviation using Median: ∑ | X – X1 | / N

## 2. Relative Measures of Dispersion

Relative Measure of Dispersion in Statistics are the values without units. A relative measure of dispersion is used to compare the distribution of two or more datasets.

The definition of the Relative Measure of Dispersion is the same as the Absolute Measure of Dispersion; the only difference is the measuring quantity.

Types of Relative Measure of Dispersion: Relative Measure of Dispersion is the calculation of the co-efficient of Dispersion, where 2 series are compared, which differ widely in their average.

The main use of the co-efficient of Dispersion is when 2 series with different measurement units are compared.

1. **Co-efficient of Range:** it is calculated as the ratio of the difference between the largest and smallest terms of the distribution, to the sum of the largest and smallest terms of the distribution.

**Formula:**

- L – S / L + S
- where L = largest value
- S= smallest value

2.** ****Co-efficient of Variation:** The coefficient of variation is used to compare the 2 data with respect to homogeneity or consistency.

**Formula:**

- C.V = (σ / X) 100
- X = standard deviation
- σ = mean

3. **Co-efficient of Standard Deviation:** The co-efficient of Standard Deviation is the ratio of standard deviation with the mean of the distribution of terms.

**Formula:**

- σ = ( √( X – X1)) / (N - 1)
- Deviation = ( X – X1)
- σ = standard deviation
- N= total number

4. **Co-efficient of Quartile Deviation:** The co-efficient of Quartile Deviation is the ratio of the difference between the upper quartile and the lower quartile to the sum of the upper quartile and lower quartile.

**Formula:**

- ( Q3 – Q3) / ( Q3 + Q1)
- Q3 = Upper Quartile
- Q1 = Lower Quartile

5. **Co-efficient of Mean Deviation****:** The co-efficient of Mean Deviation can be computed using the mean or median of the data.

Mean Deviation using Mean: ∑ | X – M | / N

Mean Deviation using Mean: ∑ | X – X1 | / N

These formulas come in handy a lot while calculating different aspects of data and when you use Python with data science, achieving this gets easier as the programming language offers various statistical packages for these.

## Why Dispersion is Important in a Statistics?

The knowledge of dispersion is vital in the understanding of statistics. It helps to understand concepts like the diversification of the data, how the data is spread, how it is maintained, and maintaining the data over the central value or central tendency.

Moreover, dispersion in statistics provides us with a way to get better insights into data distribution.

**For**** example, **3 distinct samples can have the same Mean, Median, or Range but completely different levels of variability.

## How to Calculate Dispersion?

Dispersion can be easily calculated using various dispersion measures, which are already mentioned in the types of Measures of Dispersion described above. Before measuring the data, it is important to understand the diversion of the terms and variations.

One can use the following method to calculate the dispersion:

- Mean
- Standard deviation
- Variance
- Quartile deviation

For example, let us consider two datasets:

- Data A:97,98,99,100,101,102,103
- Data B: 70,80,90,100,110,120,130

On calculating the mean and median of the two datasets, both have the same value, which is 100. However, the rest of the dispersion measures are totally different as measured by the above methods.

The range of B is 10 times higher, for instance.

## How to Represent Dispersion in Statistics?

Dispersion in Statistics can be represented in the form of graphs and pie-charts. Some of the different ways used include:

- Dot Plots
- Box Plots
- Stems
- Leaf Plots

### Example: What is the variance of the values 3,8,6,10,12,9,11,10,12,7?

Variation of the values can be calculated using the following formula:

- (σ2) = ∑ ( X − μ)2 / N
- (σ2) = 7.36

## Measure of Dispersion Example

Dispersion is one of the most important types of descriptive measures in statistics that describe the variation or spread of the data values in a data set, and the most common measures of dispersion are Range, Variance, Standard Deviation and Coefficient of variation, where Range is the difference between the largest and smallest data value in a data set.

Let's consider a dataset representing my daily commute times (in minutes): The following whole numbers only: [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]

To calculate the range I use the formula: =

Max Limit − Min Limit

## Conclusion

Understanding the measure of dispersion in statistics is crucial for interpreting data accurately. It provides insights into the variability and spread of data points, which is essential for making informed decisions. By utilizing measures such as range, variance, and standard deviation, analysts can assess the reliability and consistency of their data. This process of data analysis also improves the quality of predictions and conclusions drawn from statistical studies.

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