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How to calculate covariance in Excel?

In this Excel tutorial, you learn how to calculate covariance in Excel.

We can detect the existence of a correlation relationship by examining the covariance.

Covariance is the average of the products of the deviations of each data point pair.

Use covariance to define the relationship between two datasets.

Sample Covariance

To calculate sample covariance use the COVARIANCE.S Excel function.



In the given example sample covariance formula is =COVARIANCE.S(A2:A10,B2:B10)

Population Covariance

To calculate population covariance use the COVARIANCE.P Excel function.



In the given example sample covariance formula is =COVARIANCE.P(A2:A10,B2:B10)

In most cases, you will use sample covariance. Use covariance of population function only when it is specifically said that it is population covariance.

Using the Data analysis tool

Using functions to enumerate the covariance is effective. I prefer to use the data analysis tool for covariance calculation. It is also a convenient way to calculate the covariance of a data table. Excel will prepare a covariance report based on the values ​​you provide.

To calculate the covariance with the data analysis tool, first go to the Excel ribbon. Click the Data tab. On the right side you will find the Data Analysis button.

A window will appear. Select Covariance from the menu.

In the next window, determine the input range. Covariance input is your entire data table. If you also select the row with headings, then check the Labels in the first row option.

Select the place where you want the result as the output range. If you have free space on the worksheet, enter the cell address. Otherwise, check New Worksheet Ply.

The covariance report has appeared in the position you selected as output in the previous window.

Download a covariance calculator here.

It’s important to note that the covariance value alone does not give you a complete picture of the relationship between two variables. You should also consider other statistical measures such as the correlation coefficient, which measures the strength and direction of the linear relationship between two variables, and the coefficient of determination, which measures the proportion of the total variation in one variable that can be explained by the other variable.

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