Casey Deesel is a sports agent negotiating a contract for Titus Johnston, an athlete in the National Football League (NFL). An important aspect of any NFL contract is the amount of guaranteed money over the life of the contract. Casey has gathered data on 506 NFL athletes who have recently signed new contracts. Each observation (NFL athlete) includes values for percentage of his team's plays that the athlete is on the field (SnapPercent), the number of awards an athlete has received recognizing on-field performance (Awards), the number of games the athlete has missed due to injury (GamesMissed), and millions of dollars of guaranteed money in the athlete's most recent contract (Money, dependent variable). Casey has trained a full regression tree on 304 observations and then used the validation set to prune the tree to obtain a best-pruned tree. The best-pruned tree (as applied to the 202 observations in the validation set) is: (a) Titus Johnston's variable values are: SnapPercent = 94, Awards = 6, and GamesMissed = 3. How much guaranteed money does the regression tree predict that a player with Titus Johnson's profile should earn in his contract? If required, round your answers to two decimal places. The predicted result is $ ?? million of guaranteed money. (b) Casey feels that Titus was denied an additional award in the past season due to some questionable voting by some sports media. If Titus had won this additional award, how much additional guaranteed money would the regression tree predict for Titus versus the prediction in part (a)? An additional award would not change the amount of guaranteed money. An additional award would increase the amount of guaranteed money by $8.91 million. An additional award would increase the amount of guaranteed money by $13.99 million. An additional award would increase the amount of guaranteed money by $17.79 million. An additional award would increase the amount of guaranteed money by $26.02 million. Select correct answer.

Casey Deesel is a sports agent negotiating a contract for Titus Johnston, an athlete in the National Football League (NFL). An important aspect of any NFL contract is the amount of guaranteed money over the life of the contract. Casey has gathered data on 506 NFL athletes who have recently signed new contracts. Each observation (NFL athlete) includes values for percentage of his team's plays that the athlete is on the field (SnapPercent), the number of awards an athlete has received recognizing on-field performance (Awards), the number of games the athlete has missed due to injury (GamesMissed), and millions of dollars of guaranteed money in the athlete's most recent contract (Money, dependent variable). Casey has trained a full regression tree on 304 observations and then used the validation set to prune the tree to obtain a best-pruned tree. The best-pruned tree (as applied to the 202 observations in the validation set) is: (a) Titus Johnston's variable values are: SnapPercent = 94, Awards = 6, and GamesMissed = 3. How much guaranteed money does the regression tree predict that a player with Titus Johnson's profile should earn in his contract? If required, round your answers to two decimal places. The predicted result is $ ?? million of guaranteed money. (b) Casey feels that Titus was denied an additional award in the past season due to some questionable voting by some sports media. If Titus had won this additional award, how much additional guaranteed money would the regression tree predict for Titus versus the prediction in part (a)? An additional award would not change the amount of guaranteed money. An additional award would increase the amount of guaranteed money by $8.91 million. An additional award would increase the amount of guaranteed money by $13.99 million. An additional award would increase the amount of guaranteed money by $17.79 million. An additional award would increase the amount of guaranteed money by $26.02 million. Select correct answer.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Casey has trained a full regression tree on 304 observations and then used the validation set to prune the tree to obtain a best-pruned tree. The best-pruned tree (as applied to the 202 observations in the validation set) is:

(a)	Titus Johnston's variable values are: SnapPercent = 94, Awards = 6, and GamesMissed = 3. How much guaranteed money does the regression tree predict that a player with Titus Johnson's profile should earn in his contract?

	If required, round your answers to two decimal places.
	The predicted result is $ ?? million of guaranteed money.

(b)	Casey feels that Titus was denied an additional award in the past season due to some questionable voting by some sports media. If Titus had won this additional award, how much additional guaranteed money would the regression tree predict for Titus versus the prediction in part (a)?

	An additional award would not change the amount of guaranteed money. An additional award would increase the amount of guaranteed money by $8.91 million. An additional award would increase the amount of guaranteed money by $13.99 million. An additional award would increase the amount of guaranteed money by $17.79 million. An additional award would increase the amount of guaranteed money by $26.02 million. Select correct answer.