One way to quantify the relationship between two variables is to use the Pearson correlation coefficient which is a measure of the linear association between two variables.

It always takes on a value between -1 and 1 where:

- -1 indicates a perfectly negative linear correlation between two variables
- 0 indicates no linear correlation between two variables
- 1 indicates a perfectly positive linear correlation between two variables

To determine if a correlation coefficient is statistically significant you can perform a correlation test, which involves calculating a t-score and a corresponding p-value.

The formula to calculate the t-score is:

**t = r√(n-2) / (1-r ^{2})**

where:

**r:**Correlation coefficient**n:**The sample size

The p-value is calculated as the corresponding two-sided p-value for the t-distribution with n-2 degrees of freedom.

The following step-by-step example shows how to perform a correlation test in Excel.

**Step 1: Enter the Data**

First, let’s enter some data values for two variables in Excel:

**Step 2: Calculate the Correlation Coefficient**

Next, we can use the **CORREL()** function to calculate the correlation coefficient between the two variables:

The correlation coefficient between the two variables turns out to be **0.803702**.

This is a highly positive correlation coefficient, but to determine if it’s statistically significant we need to calculate the corresponding t-score and p-value.

**Step 3: Calculate the Test Statistic and P-Value**

Next, we can use the following formulas to calculate the test statistic and the corresponding p-value:

The test statistic turns out to be **4.27124 **and the corresponding p-value is **0.001634**.

Since this p-value is less than .05, we have sufficient evidence to say that the correlation between the two variables is statistically significant.

**Additional Resources**

How to Create a Correlation Matrix in Excel

How to Calculate Spearman Rank Correlation in Excel

How to Calculate Rolling Correlation in Excel

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