# How to Calculate Correlation in Excel

The correlation coefficient is a statistical measure that represents the strength and direction of the relationship between two variables. In Microsoft Excel, you can calculate the correlation coefficient using the "CORREL" function. In this post I'm presenting to you the steps to calculate the correlation coefficient in Excel.

For you to make appropriate correlation calculation, it is important to have two different columns. It makes it easier to know if there is any correlation between them. We are going to use unit price and quantity of sold units to determine if there is any correlation between them.

## The data

Layout the columns, we have chosen unit price and sold units for this calculation. The data should be numerical, and the two variables should be related.

## The formula

Select the cell where you want the result of the correlation coefficient to be displayed.

Click on an empty column on the unit sold (1), and type in =CORREL(columns that are relevant to the columns ex. A1:A4, B1:B3), which is number 2 in the picture below.

=CORREL(A4:A14,B4:B14)

The correlation coefficient will be a number between -1 and 1. A value of 1 indicates a perfect positive correlation, meaning that as one variable increases, the other variable increases as well. A value of -1 indicates a perfect negative correlation, meaning that as one variable increases, the other variable decreases. A value of 0 indicates no correlation.

Note that you can also use the "PEARSON" function in Excel to calculate the correlation coefficient. The "PEARSON" function works in the same way as the "CORREL" function.

Now you can create correlation chart based on above correlation data.

In conclusion, calculating the correlation coefficient in Excel is simple and straightforward using the "CORREL" or "PEARSON" function. By analyzing the correlation coefficient, you can determine the strength and direction of the relationship between two variables, and use this information to make informed decisions or predictions.

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