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The mean values for the given data are:

- \( \bar{x} \) (mean of 40-yard dash times) = 4.508 seconds (rounded to three decimal places)
- \( \bar{y} \) (mean of 225 lb. bench press reps) = 21.333 reps

Next, we can address part (b) of the problem.

Would you like me to continue with the regression analysis or any clarifications on part (a)? 

Here are 5 related questions to expand on this:
1. How are means \( \bar{x} \) and \( \bar{y} \) used in regression analysis?
2. What is the formula for the least-squares regression line?
3. How do you interpret the slope and intercept in a regression equation?
4. Can outliers affect the least-squares regression line, and how would you identify them here?
5. What are some common applications of regression in sports data analysis?

**Tip:** Always keep data properly formatted to avoid rounding errors during calculations.

Consider the following data for players at the linebacker position at a recent NFL Combine, where x = the player's 40-Yard Dash time and y = the player's number of reps at the 225 lb. bench press. a. Calculate x̄ and ȳ. b. Using x̄ and ȳ from part a, explain why ŷ = 310.523 - 57.394x can or cannot be the equation of the least-squares regression line for these data.

Learn how to calculate the mean 40-yard dash time and 225 lb. bench press reps for NFL linebackers, and assess whether the regression equation ŷ = 310.523 - 57.394x is valid. This problem involves key statistical concepts like mean and regression analysis.

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