A manufacturer of computer memory chips produces chips in lots of 1000. If nothing has gone wrong in the manufacturing process, at most 5 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. Suppose that whether or not a lot is good is random, that the long-run fraction of lots that are good is 92%, and that whether each lot is good is independent of whether any other lot or lots are good. Assume that the sample drawn from a lot is independent of whether the lot is good or bad. To simplify the problem even more, assume that good lots contain exactly 5 defective chips, and that bad lots contain exactly 60 defective chips. (Continues previous problem.) The number of lots the manufacturer has to produce to get one good lot that is not rejected by the test has a (020) distribution. with parameters (021) (Continues previous problem.) The expected number of lots the manufacturer must make to get one good lot that is not rejected by the test is (022) C] (Continues previous problem.) With this test and this mix of good and bad lots. among the lots that pass the test, the long-run fraction of lots that are actually bad is (023) C]