Acorrelation coefficient is a measure of the linear association between two variables. It can take 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

You can use the following steps to calculate the correlation coefficient between two variables on a TI-84 calculator:

Step 1: Turn on diagnostics.

First, we need to turn on diagnostics. To do so, press2nd and then press the number 0. This will take us to the CATALOG screen.

CATALOG screen on TI-84 calculator

Scroll down to DiagnosticOn and pressENTER.

DiagnosticOn on TI-84 calculator

Then pressENTER once more.

Diagnostics on TI-84 calculator

The diagnostics are now turned on so that we can calculate the correlation coefficient between two variables.

Step 2: Input the data.

Next, we need to enter the data values for our two variables. PressStat and then pressEDIT. Enter the values for the first variable in column L1 and the values for the second variable in column L2:

Correlation coefficient in TI-84

Step 3: Find the correlation coefficient.

Next, we will calculate the correlation coefficient between the two variables. PressStat and then scroll over to CALC. Then scroll down to 8: Linreg(a+bx) and pressEnter.

Correlation coefficient on a TI-84 calculator

For Xlist and Ylist, make sure L1 and L2 are selected since these are the columns we used to input our data. LeaveFreqListblank. Scroll down toCalculateand pressEnter.

Correlation calculation in TI-84 calculator

On the new screen we can see that the correlation coefficient (r) between the two variables is0.9145.

Correlation in TI-84 calculator

How to Interpret a Correlation Coefficient

The following table shows the rule of thumb for interpreting the strength of the relationship between two variables based on the value ofr:

Absolute value ofr Strength of relationship
r < 0.25 No relationship
0.25 < r < 0.5 Weak relationship
0.5 < r < 0.75 Moderate relationship
r > 0.75 Strong relationship

In our example, a correlation coefficient of0.9145indicates a strong positive relationship between the two variables.