In an article that appears on the website of the American Statistical Asso-ciation (www.amstat.org/meetings/jsm/2000 /usei/gunn.pdf, retrieved April 25, 2017),Carlton Gunn, a public defender in Seattle, Washington, wrote about how he uses statis-tics in his work as an attorney. He states:I personally have used statistics in trying to challenge the reliability of drugtesting results. Suppose the chance of a mistake in the taking and processingof 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? Notnecessarily. If the vast majority of all tests given- say 99 in 100-are trulyclean, then you get one false dirty and one true dirty in every 100 tests, sothat half of the dirty tests are false.TD = event that the test result is dirtyTC= event that the test result is cleanD = event that the person tested is actually dirtyC = event that the person tested is actually cleanP(TD|D) = 0.99 P(C) = 0.99P(TD|C) = 0.01 P(D)=0.01What is the probability of a random test resulting in dirty?(b) 4 What is the probability of person being clean given that their test re-sult is dirty? Is this consistent with the quote given?

(a) The probability of a random test resulting in dirty, is given by, PTD=PC·PTDC+PD·PTDD Substitute…

Answered: In an article that appears on the…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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In an article that appears on a website,† Carlton Gunn, a public defender in Seattle, Washington, wrote about how he uses statistics in his work as an attorney. He states the following. 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 given below. 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, compute the following…
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