Software filters rely heavily on "blacklists" (lists of known "phishing" URLS) to detect fraudulent e-mails. But such filters typically catch only 21 percent of phishing URLS. Jason receives 18 phishing e-mails. (e) What is the expected number that would be caught by such a filter? (Round your answer to 2 decimal places.) Expected number (b) What is the chance that such a filter would detect none of them? (Round your answer to 5 decimal places.) Probability
Software filters rely heavily on "blacklists" (lists of known "phishing" URLS) to detect fraudulent e-mails. But such filters typically catch only 21 percent of phishing URLS. Jason receives 18 phishing e-mails. (e) What is the expected number that would be caught by such a filter? (Round your answer to 2 decimal places.) Expected number (b) What is the chance that such a filter would detect none of them? (Round your answer to 5 decimal places.) Probability
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Transcribed Image Text:Software filters rely heavily on "blacklists" (lists of known "phishing" URLs) to detect fraudulent e-mails. But such filters typically catch only 21 percent of phishing URLs. Jason receives 18 phishing e-mails.
(a) What is the expected number that would be caught by such a filter? (Round your answer to 2 decimal places.)
| Expected number | _______________ |
(b) What is the chance that such a filter would detect none of them? (Round your answer to 5 decimal places.)
| Probability | _______________ |
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