A news reporter wants to assess the top 30 college quarterbacks. The reporter recorded the number of plays and the number of passes a quarterback completed in one season. This sample data is provided below.

Use Excel to calculate the correlation coefficient \( r \) between the two data sets. Round your answer to two decimal places.

| Plays | Passes |
|-------|---------|
| 519 | 104.3 |
| 678 | 109.4 |
| 373 | 50.7 |
| 319 | 43.7 |
| 496 | 83.6 |
| 430 | 47.4 |
| 542 | 49.4 |
| 357 | 74.7 |
| 642 | 99.6 |
| 592 | 54.3 |
| 417 | 62.5 |
| 701 | 84.7 |
| 457 | 48.0 |
| 815 | 119.8 |
| 394 | 45.8 |
| 514 | 59.4 |
| 527 | 55.7 |
| 494 | 44.4 |
| 487 | 49.6 |
| 305 | 43.5 |
| 565 | 46.7 |
| 551 | 68.9 |
| 644 | 86.3 |
| 403 | 45.7 |
| 605 | 51.7 |
| 819 | 100.5 |
| 479 | 67.5 |
| 468 | 46.6 |
| 544 | 57.7 |
| 532 | 68.1 |

\( r = \underline{\hspace{2cm}} \)

To calculate the correlation coefficient
the number of plays and the number of passes completed in Excel, input the data into two columns and use the CORREL function. Round the result to two decimal places.

To calculate the correlation coefficient r between the number of plays and the number of passes completed by the top college quarterbacks using Excel, you would need to input the given data into two columns in Excel and then use the CORREL function to compute r. However, since I cannot perform the actual calculation here, I can guide you through the steps you would follow to calculate r on your own:

Enter the data for 'Plays' in Column A and data for 'Passes' in Column B.
Click on an empty cell where you wish to display the correlation coefficient.
Type in =CORREL(A:A, B:B) and press Enter. This will give you the correlation coefficient.
Round this number to two decimal places as required.

The interpretation of the correlation coefficient r will depend on its value. A value close to 1 or -1 indicates a strong linear relationship, while a value close to 0 indicates a weak linear relationship. Additionally, you can square the correlation coefficient to obtain the coefficient of determination (r²), representing the percentage of the variance in one variable that is predictable from the other variable.

If, for example, after performing the calculation, you found r to be 0.70, this would indicate a strong, positive relationship between the number of plays and the number of passes, as 0.70 is greater than the cut-off value typically used (such as 0.423 for a sample size of 30 at a 95% confidence level). Therefore, you could conclude there is a significant linear relationship based on this hypothetical value.