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Teddy Bower is an outdoor clothing and accessories chain that purchases a line of parkas at $ 1 1 each from its Asian supplier, TeddySports.

Teddy Bower is an outdoor clothing and accessories chain that purchases a line of parkas at $11 each from its Asian supplier, TeddySports. Unfortunately, at the time of the order placement, demand is still uncertain: Teddy Bower forecasts that its demand is normally distributed with a mean of 2,200 and a standard deviation of 1,300. Teddy Bower sells these parkas at $24 each. Unsold parkas have little salvage value; Teddy Bower simply gives them away to a charity (and also doesnt collect a tax benefit for the donation). Use Table 13.4.
Note: If a part of the question specifies whether to use Table 13.4, or to use Excel, then credit for a correct answer will depend on using the specified method.
What is the probability this parka turns out to be a dog, defined as a product that sells less than half of the forecast? Use Excel.
Note: Round your answer to 4 decimal places.
How many parkas should Teddy Bower buy from TeddySports to maximize expected profit? Use Table 13.4.
If Teddy Bower orders 3,000 parkas, what is the in-stock probability? Use Excel.
Note: Round your answer to 4 decimal places.
If Teddy Bower orders 3,000 parkas, what is expected leftover inventory? Use Excel.
Note: Round your answer to 2 decimal places.
If Teddy Bower orders 3,000 parkas, what is expected sales? Use Excel.
Note: Round your answer to 2 decimal places.
If Teddy Bower orders 3,000 parkas, what is expected profit? Use Excel.
Note: Round your answer to 2 decimal places.
How many parkas should Teddy Bower order to ensure a 98.5% in-stock probability? how many parkas should Teddy Bowers order? Use Table 13.4.z F(z) I(z)
-4.00.00000.0000
-3.90.00000.0000
-3.80.00010.0000
-3.70.00010.0000
-3.60.00020.0000
-3.50.00020.0001
-3.40.00030.0001
-3.30.00050.0001
-3.20.00070.0002
-3.10.00100.0003
-3.00.00130.0004
-2.90.00190.0005
-2.80.00260.0008
-2.70.00350.0011
-2.60.00470.0015
-2.50.00620.0020
-2.40.00820.0027
-2.30.01070.0037
-2.20.01390.0049
-2.10.01790.0065
-2.00.02280.0085
-1.90.02870.0111
-1.80.03590.0143
-1.70.04460.0183
-1.60.05480.0232
-1.50.06680.0293
-1.40.08080.0367
-1.30.09680.0455
-1.20.11510.0561
-l.10.13570.0686
-1.00.15870.0833
-0.90.18410.1004
-0.80.21190.1202
-0.70.24200.1429
-0.60.27430.1687
-0.50.30850.1978
-0.40.34460.2304
-0.30.38210.2668
-0.20.42070.3069
-0.10.46020.3509
0.00.50000.3989
0.10.53980.4509
0.20.57930.5069
0.30.61790.5668
0.40.65540.6304
0.50.69150.6978
0.60.72570.7687
0.70.75800.8429
0.80.78810.9202
0.90.81591.0004
1.00.84131.0833
1.10.86431.1686
1.20.88491.2561
1.30.90321.3455
1.40.91921.4367
1.50.93321.5293
1.60.94521.6232
1.70.95541.7183
1.80.96411.8143
1.90.97131.9111
2.00.97722.0085
2.10.98212.1065
2.20.98612.2049
2.30.98932.3037
2.40.99182.4027
2.50.99382.5020
2.60.99532.6015
2.70.99652.7011
2.80.99742.8008
2.90.99812.9005
3.00.99873.0004
3.10.99903.1003
3.20.99933.2002
3.30.99953.3001
3.40.99973.4001
3.50.99983.5001
3.60.99983.6000
3.70.99993.7000
3.80.9999

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