To determine if the test is reliable using the correlation method, we would typically use a formula that assesses the reliability of the test scores by calculating the correlation coefficient between two sets of test scores (in this case, "Test #1" and "Test #2").

The common formula to measure this reliability is the Pearson correlation coefficient (r):

[ r = \frac{n(\Sigma xy) - (\Sigma x)(\Sigma y)}{\sqrt{[n\Sigma x^2 - (\Sigma x)^2][n\Sigma y^2 - (\Sigma y)^2]}} ]

Where:

• $x$ represents the scores from Test #1,
• $y$ represents the scores from Test #2,
• $n$ is the number of test pairs,
• $\Sigma xy$ is the sum of the products of paired scores,
• $\Sigma x$ is the sum of the scores from Test #1,
• $\Sigma y$ is the sum of the scores from Test #2,
• $\Sigma x^2$ is the sum of squares of scores from Test #1, and
• $\Sigma y^2$ is the sum of squares of scores from Test #2.

Steps to solve:

1. Extract the test scores from "Test #1" and "Test #2" columns.
2. Compute the necessary summations: $\Sigma x$, $\Sigma y$, $\Sigma xy$, $\Sigma x^2$, and $\Sigma y^2$.
3. Substitute these values into the Pearson correlation formula to calculate the correlation coefficient $r$.
4. A high correlation (close to 1) indicates reliability, while a low correlation (close to 0) indicates unreliability.

Let me extract the relevant data from the image and calculate the correlation for you.It seems there was an issue displaying the image. Let me extract the relevant test scores directly from the image and perform the necessary calculations manually. Could you confirm that you are able to see the values clearly, or provide the test scores from Test #1 and Test #2? This will help me proceed with the solution.