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 = 84, Awards = 7, and GamesMissed = 2. 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 increase the amount of guaranteed money by $8.91 million. An additional award would increase the amount of guaranteed money by $11.81 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 $32.00 million. An additional award would not change the amount of guaranteed money. - Select your answer -I.II.III.IV.V.

Question

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 = 84, Awards = 7, and GamesMissed = 2. 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 increase the amount of guaranteed money by $8.91 million. An additional award would increase the amount of guaranteed money by $11.81 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 $32.00 million. An additional award would not change the amount of guaranteed money. - Select your answer -I.II.III.IV.V.

(c)	As Casey reviews the best-pruned tree, he is confused by the leaf node corresponding to the sequence of decision rules of "SnapPercent ≥ 90.28, SnapPercent ≥ 95.37, Awards < 7.25, GamesMissed < 2.5." This sequence of decision rules results in an estimate of $47.83 million of guaranteed money, but the tree states that zero observations occur in the corresponding partition. If zero observations occur in this partition, how can the regression tree provide an estimate of $50 million? Explain this part of the regression tree to Casey by referring to how the best-pruned tree is obtained.

	The predicted guaranteed money of $47.83 million for observations satisfying "SnapPercent ≥ 90.28, SnapPercent ≥ 95.37, Awards < 7.25, GamesMissed < 2.5" is based on the average guaranteed money of the observations in the - Select your answer -training or validation set that satisfy this sequence of decision rules. The best-pruned tree is obtained by - Select your answer -removing leaf nodes from or adding leaf nodes to the initial regression tree to obtain the tree with the - Select your answer -fewest or greatest leaf nodes while achieving the minimum classification error rate on the - Select your answer -training or validation set. In this case, the - Select your answer -training or validation set has zero observations that satisfy "SnapPercent ≥ 90.28, SnapPercent ≥ 95.37, Awards < 7.25, GamesMissed < 2.5" which just means that this leaf node - Select your answer -does not contributes or contributes to the classification error rate of this tree.