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wrote...
Posts: 206
3 weeks ago
Improving Efficiency A distribution company considers hiring a national training consultant in hopes of improving efficiency in deliveries. The national consultant agrees to work with 30 drivers for one week as part of a trial before the manufacturing company makes a decision about the training program. The training program will be implemented if the average completed number of deliveries increases by more than 12 deliveries per day per driver. The distribution company manager will test a hypothesis using α = 0.02.
a. Write appropriate hypotheses (in words and in symbols).
b. In this context, which do you consider to be more serious – a Type I or a Type II error? Explain briefly.
c. After this trial produced inconclusive results the manager decided to test the training program again with another group of drivers. Describe two changes he could make in the trial to increase the power of the test, and explain the disadvantages of each.
Textbook 

Stats: Modeling the World


Edition: 4th
Authors:
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wrote...
3 weeks ago
a. H0: μd = 12HA: μd > 12
b. While a Type II error would represent a lost opportunity to improve delivery efficiency, a Type I error would be very expensive for the manufacturing company. A Type I error would mean that the manager rejected the null hypothesis when in fact the null hypothesis is true. In this situation, by rejecting the null hypothesis the company thought the training improved efficiency, so they paid for the consultant to train all drivers. In reality, the training did not improve efficiency so the company wasted money on training useless.
c. To increase the power of the test, we could increase the level of significance (α), or increase the sample size. Increasing the level of significance, could lead to adopting a training method that actually does not improve delivery efficiency. By increasing the sample size, the trial cost would increase and the trial might take more time.
wrote...
3 weeks ago
Helps a lot... Now I'm ready for my quiz
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