MySQL VAR_SAMP Function

MySQL VAR_SAMP Aggregate Function is useful to calculate the Sample Variance of total records (or rows) selected by the SELECT Statement.

MySQL VAR_SAMP Formula

The mathematical formula behind the VAR_SAMP to calculate the sample variance is

--Calculating Mean or Average
Mean = Sum of each individual / Total number of items

--Calculating the Sample Variance
Sample Variance = ( (OriginalValue – Mean)² + (OriginalValue – Mean)² +.... ) / (Total number of items - 1)

The syntax behind the VAR_SAMP function in MySQL is

-- VAR_SAMP in MySQL Syntax
SELECT VAR_SAMP(Column_Name)
FROM Source;

How to write VAR_SAMP function to calculate the sample Variance with an example using the below-shown data?.

MySQL VAR_SAMP Example 1

MySQL VAR_SAMP Example

The VAR_SAMP function returns the Simple Variance of total records present in a specified column. For example, the below query calculates the Sample Variance of all the records present in Yearly_Income from Customer details table.

-- MySQL VAR_SAMP example
SELECT VAR_SAMP(Yearly_Income) AS `Simple income Variance`
FROM customerdetails;
MySQL VAR_SAMP Function Example 1

MySQL VAR_SAMP Group By Example

In general, the VAR_SAMP function calculates the Sample Variance of products belongs to a particular category or color, etc. In this situation, you can use the GROUP BY Clause in the MySQL to group the products by category. And next, use VAR_SAMP Aggregate Function to calculate the Sample Variance.

-- MySQL VAR_SAMP Function Example
USE company;
SELECT  Profession,
        VAR_SAMP(Yearly_Income)
FROM customerdetails
GROUP BY Profession;

The above VAR_SAMP SELECT Statement query group the Customers by their Profession, and calculates their Sample Variance

MySQL VAR_SAMP Function Example 2

We are taking the Software Developer profession, and show you the VAR_SAMP output.

— Calculating Mean
Mean = (70000 + 79,000) / 2
Mean = 74,500

Here, we are calculating Sample Variance.
Variance = (70,000 – 74,500)² + (79,000 – 74,500)² / (2-1)
Variance = 40,500,000