A manufacturer of computer memory chips produces chips in lots of 1000. If nothing has gone wrong in the manufacturing process, at most 7 chips each lot would be defective, but if something does go wrong, there could be far more defective chips. If something goes wrong with a given lot, they discard the entire lot. It would be prohibitively expensive to test every chip in every lot, so they want to make the decision of whether or not to discard a given lot on the basis of the number of defective chips in a simple random sample. They decide they can afford to test 100 chips from each lot. You are hired as their statistician. There is a tradeoff between the cost of erroneously discarding a good lot, and the cost of warranty claims if a bad lot is sold. The next few problems refer to this scenario. I know that the type 1 error is a good lot gets discarded and a type 2 error is a bad lot isn't discarded. I also figured out that sample size has a hypergeometric distribution because N=1,000, G<=5, and n=100. I tried answering the questions three different times (each time I was incorrect) and I am having difficulty. Please help me!
Problem 11. (Continues previous problem.) To have a chance of at most 1% of discarding a lot given that the lot is good, the test should reject if the number of defectives in the sample of size 100 is greater than or equal to
Problem 12. (Continues previous problem.) In that case, the chance of rejecting the lot if it really has 30 defective chips is
Problem 13. (Continues previous problem.) In the long run, the fraction of lots with 5 defectives that will get discarded erroneously by this test is
Problem 14. (Continues previous problem.) The smallest number of defectives in the lot for which this test has at least a 99% chance of correctly detecting that the lot was bad is