Question

1 college football coach wants to know if the is a correlation between his players' leg strength and the time it takes for them to sprint 40 yards. he sets up the following test and records the data:

Every day for a week, he counts how many times each player can leg press 350 pounds. The following week, he has each player sprint 40 yards every day. The tables shows the average number of leg-press repetitions and the average 40-yard dash time (in seconds) for seven randomly selected players. What is the equation of the line of best fit? How many seconds should he expect a player to take to r

Answer 1

A college football coach wants to know if the is a correlation between his players' leg strength and the time it takes for them to sprint 40 yards. he sets up the following test and records the data:

Every day for a week, he counts how many times each player can leg press 350 pounds. The following week, he has each player sprint 40 yards every day. The tables shows the average number of leg-press repetitions and the average 40-yard dash time (in seconds) for seven randomly selected players. What is the equation of the line of best fit? How many seconds should he expect a player to take to run 40 yards if that player can do 22 leg-press repetitions?

Leg Press (reps)|40-yard Dash (s)
15 |5.2
18 |6.3
8 |6.8
30 |8.2
26 |8.0
12 |5.3
21 |5.9

Answer 2

Please help im very behind and confused...

Answer 3

You have two types of data (Leg Presses and 40-Yard Dashes) for each of 7 athletes. Your job is to calculate a regression line for this data.

Somewhere in your study materials (whether on paper or online) you'll find examples of how to calculate the parameters of a regression line. You'll likely have to find the standard deviations of both sets of data, as well as the correlation coefficient, r.

If you don't already have formulas for the standard deviations and the correlation coefficient in front of you, could you look them up, please?

Answer 4

hint:
in order to understand the equation of best fit, please try to construct a scatter plot using the data above

Answer 5

@mathmale your advise was excellent and right in-line with what I was thinking. I just couldn't figure out how to find the r and R^2 values.

As it turns out excel does it very painlessly, now here is my question, which I assume is obvious, but here goes.

The second order polynomial has a higher R^2, than the linear regression, so the polynomial if a better fit. Then I did a third order polynomial and it had an even better fit.

So, never mind I was going to ask when do you quit, but the fourth order had the same R^2 as the third order.

Answer 6

How nice to hear from you! I'm a retired EE also, although 98% of my career consisted of college level instruction of young deaf adults.

Looks like the Law of Diminishing Returns fits this fit problem (pun intended). No point in continuing with increasingly complex fits when one's latest result isn't an improvement on the previous one!

Happy New Year!

Answer 7

Thanks.