Path To College Mathematics
1st Edition
ISBN: 9780134654409
Author: Martin-Gay, K. Elayn, 1955-
Publisher: Pearson,
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Chapter C, Problem 49ES
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Chapter C Solutions
Path To College Mathematics
Ch. C - Prob. 1ESCh. C - Prob. 2ESCh. C - Prob. 3ESCh. C - Prob. 4ESCh. C - Prob. 5ESCh. C - Prob. 6ESCh. C - Prob. 7ESCh. C - Prob. 8ESCh. C - Prob. 9ESCh. C - Prob. 10ES
Ch. C - Prob. 11ESCh. C - Prob. 12ESCh. C - Prob. 13ESCh. C - Prob. 14ESCh. C - Prob. 15ESCh. C - Prob. 16ESCh. C - Prob. 17ESCh. C - Prob. 18ESCh. C - Prob. 19ESCh. C - Prob. 20ESCh. C - Prob. 21ESCh. C - Prob. 22ESCh. C - Prob. 23ESCh. C - Prob. 24ESCh. C - Prob. 25ESCh. C - Prob. 26ESCh. C - Prob. 27ESCh. C - Prob. 28ESCh. C - Prob. 29ESCh. C - Prob. 30ESCh. C - Prob. 31ESCh. C - Prob. 32ESCh. C - Prob. 33ESCh. C - Prob. 34ESCh. C - Prob. 35ESCh. C - Prob. 36ESCh. C - Prob. 37ESCh. C - Prob. 38ESCh. C - Prob. 39ESCh. C - Prob. 40ESCh. C - Prob. 41ESCh. C - Prob. 42ESCh. C - Prob. 43ESCh. C - Prob. 44ESCh. C - Prob. 45ESCh. C - Prob. 46ESCh. C - Prob. 47ESCh. C - Prob. 48ESCh. C - Prob. 49ESCh. C - Prob. 50ESCh. C - 12,000 good assembly line products to 40 defective...Ch. C - Prob. 52ESCh. C - Prob. 53ESCh. C - Prob. 54ESCh. C - Prob. 55ESCh. C - Prob. 56ESCh. C - Prob. 57ESCh. C - Prob. 58ESCh. C - Prob. 59ESCh. C - Prob. 60ESCh. C - Prob. 61ESCh. C - Prob. 62ESCh. C - Prob. 63ESCh. C - Prob. 64ESCh. C - Prob. 65ESCh. C - Prob. 66ESCh. C - Prob. 67ESCh. C - Prob. 68ESCh. C - Prob. 69ESCh. C - Prob. 70ES
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- Spam filters are built on principles similar to those used in logistic regression. We fit a probability that each message is spam or not spam. We have several variables for each email. Here are a few: to_multiple=1 if there are multiple recipients, winner=1 if the word 'winner' appears in the subject line, format=1 if the email is poorly formatted, re_subj=1 if "re" appears in the subject line. A logistic model was fit to a dataset with the following output: Estimate SE Z Pr(>|Z|) (Intercept) -0.8161 0.086 -9.4895 0 to_multiple -2.5651 0.3052 -8.4047 0 winner 1.5801 0.3156 5.0067 0 format -0.1528 0.1136 -1.3451 0.1786 re_subj -2.8401 0.363 -7.824 0 (a) Write down the model using the coefficients from the model fit.log_odds(spam) = -0.8161 + -2.5651 + to_multiple + 1.5801 winner + -0.1528 format + -2.8401 re_subj(b) Suppose we have an observation where to_multiple=0, winner=1, format=0, and re_subj=0. What is the predicted probability that this message is spam?…arrow_forwardConsider an event X comprised of three outcomes whose probabilities are 9/18, 1/18,and 6/18. Compute the probability of the complement of the event. Question content area bottom Part 1 A.1/2 B.2/18 C.16/18 D.16/3arrow_forwardI need help making sure that I explain this part accutartly.arrow_forward
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