In an article that appears on the web site of the american statistical association,† carlton gunn, a public defender in seattle, washington, wrote about how he uses statistics in his work as an attorney. he states: "i personally have used statistics in trying to challenge the reliability of drug testing results. suppose the chance of a mistake in the taking and processing of a urine sample for a drug test is just 1 in 100. and your client has a "dirty" (i. e., positive) test result. only a 1 in 100 chance that it could be wrong? not necessarily. if the vast majority of all tests given—say 99 in 100—are truly clean, then you get one false dirty and one true dirty in every 100 tests, so that half of the dirty tests are false." define the following events as: td = event that the test result is dirty tc = event that the test result is clean d = event that the person tested is actually dirty c = event that the person tested is actually clean (a) using the information in the quote, find the values of each of the following. (i) p(td|d) (ii) p(td|c) (iii) p(c) (iv) p(d) (b) use the law of total probability to find p(td). (c) use bayes' rule to evaluate p(c|td). is this value consistent with the argument given in the quote? explain. yes, half of the dirty tests are on people who are in fact clean. no, less than half of the dirty tests are on people who are in fact clean. no, more than half of the dirty tests are on people who are in fact clean.