Decision Trees DOC

Title	Decision Trees
Author	Ale Zhang
Pages	1
File Size	34.5 KB
File Type	DOC
Total Downloads	37
Total Views	246

Summary

Description

Sarah King, president of King Electronics, Inc., has two design options for her new line of high-resolution cathode- ray tubes (CRTs) for computer-aided design workstations. The life cycle sales forecast for the CRT is 100.000 units. * Design option A has a 0.90 probability of yielding 59 good CRTs per 100 and a .10 probability of yielding 64 food CRTs per 100. This design will cost $1.000.000. * Design option B has 0.80 probability of yieldin 64 good units per 100 and a 0.20 probability of yielding 59 good units per 100. This design will cost $1.350.000. Good or bad, each CRT will cost $75. Each good CRT will sell for $150. Bad CRTs are destroyed and have no salvage value. Because units break up when thrown in the trash, there is little disposal cost. Therefore, we ignore any disposal costs in this problem. Option A 0.9 0.1 $ 8.850.000 = total cost: 59.000 good units x 150.000 wins/unit - $ 7.500.000 = produce cost: 100.000 bought units x 75 cost/unit - $ 1.000.000 = CAD cost $ 350.000 = net result $ 9.600.000 = total cost: 64.000 good units x 150.000 wins/unit - $ 7.500.000 = produce cost: 100.000 bought units x 75 cost/unit - $ 1.000.000 = CAD cost $ 350.000 = net result 59% 64% Option B 0.8 0.2 $ 9.600.000 = total cost: 64.000 good units x 150.000 wins/unit - $ 7.500.000 = produce cost: 100.000 bought units x 75 cost/unit - $ 1.350.000 = CAD cost $ 750.000 = net result $ 8.850.000 = total cost: 59.000 good units x 150.000 wins/unit - $ 7.500.000 = produce cost: 100.000 bought units x 75 cost/unit - $ 1.350.000 = CAD cost $ 0 = net result 64% 59% $ 315.000 $ 110.000 $ 600.000 $ 0 $ 425.000 $ 600.000...