DEVELOP.MATH(3 VOLS) CUSTOM-W/MML <IC<
16th Edition
ISBN: 9781323235911
Author: BITTINGER
Publisher: Pearson Custom Publishing
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Chapter O, Problem 20ES
To determine
To calculate: The addition of the expression
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I need help in ensuring that I explain it propleryy in the simplifest way as possible
(c) Because logistic regression predicts probabilities of outcomes, observations used to build a logistic regression model need not be independent.
A. false: all observations must be independent
B. true
C. false: only observations with the same outcome need to be independent
I ANSWERED: A. false: all observations must be independent.
(This was marked wrong but I have no idea why. Isn't this a basic assumption of logistic regression)
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Chapter O Solutions
DEVELOP.MATH(3 VOLS) CUSTOM-W/MML <IC<
Ch. O - Prob. 1DECh. O - Prob. 2DECh. O - Prob. 3DECh. O - Prob. 4DECh. O - Prob. 5DECh. O - Prob. 6DECh. O - Prob. 7DECh. O - Prob. 8DECh. O - Prob. 9DECh. O - Prob. 10DE
Ch. O - Prob. 11DECh. O - Prob. 12DECh. O - Prob. 13DECh. O - Prob. 14DECh. O - Prob. 15DECh. O - Prob. 16DECh. O - Prob. 17DECh. O - Prob. 18DECh. O - Simplify.
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Ch. O - Prob. 20DECh. O - Prob. 21DECh. O - Prob. 22DECh. O - Prob. 23DECh. O - Prob. 24DECh. O - Prob. 25DECh. O - Find the conjugate.
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Ch. O - Prob. 27DECh. O - Prob. 28DECh. O - Prob. 29DECh. O - Prob. 30DECh. O - Prob. 31DECh. O - Prob. 32DECh. O - Prob. 33DECh. O - Prob. 34DECh. O - Prob. 36DECh. O - Prob. 37DECh. O - Prob. 38DECh. O - Prob. 1ESCh. O - Prob. 2ESCh. O - Prob. 3ESCh. O - Prob. 4ESCh. O - Prob. 5ESCh. O - Prob. 6ESCh. O - Prob. 7ESCh. O - Prob. 8ESCh. O - Prob. 9ESCh. O - Prob. 10ESCh. O - Prob. 11ESCh. O - Prob. 12ESCh. O - Prob. 13ESCh. O - Prob. 14ESCh. O - Prob. 15ESCh. O - Prob. 16ESCh. O - Prob. 17ESCh. O - Prob. 18ESCh. O - Prob. 19ESCh. O - Prob. 20ESCh. O - Prob. 21ESCh. O - Prob. 22ESCh. O - Prob. 23ESCh. O - Prob. 24ESCh. O - Prob. 25ESCh. O - Prob. 26ESCh. O - Prob. 27ESCh. O - Prob. 28ESCh. O - Prob. 29ESCh. O - Prob. 30ESCh. O - Prob. 31ESCh. O - Prob. 32ESCh. O - Prob. 33ESCh. O - Prob. 34ESCh. O - Prob. 35ESCh. O - Prob. 36ESCh. O - Prob. 37ESCh. O - Prob. 38ESCh. O - Prob. 39ESCh. O - Prob. 40ESCh. O - Prob. 41ESCh. O - Prob. 42ESCh. O - Prob. 43ESCh. O - Prob. 44ESCh. O - Prob. 45ESCh. O - Prob. 46ESCh. O - Prob. 47ESCh. O - Prob. 48ESCh. O - Prob. 50ESCh. O - Prob. 51ESCh. O - Prob. 52ESCh. O - Prob. 53ESCh. O - Prob. 54ESCh. O - Prob. 55ESCh. O - Prob. 56ESCh. O - Prob. 57ESCh. O - Prob. 58ESCh. O - Prob. 59ESCh. O - Prob. 60ESCh. O - Prob. 61ESCh. O - Prob. 62ESCh. O - Prob. 63ESCh. O - Prob. 64ESCh. O - Prob. 65ESCh. O - Prob. 66ESCh. O - Prob. 67ESCh. O - Prob. 68ESCh. O - Prob. 69ESCh. O - Prob. 70ESCh. O - Prob. 71ESCh. O - Prob. 72ESCh. O - Prob. 73ESCh. O - Prob. 74ESCh. O - Prob. 75ESCh. O - Prob. 76ESCh. O - Prob. 77ESCh. O - Prob. 78ESCh. O - Prob. 79ESCh. O - Prob. 80ESCh. O - Prob. 81ESCh. O - Prob. 82ESCh. O - Prob. 83ESCh. O - Prob. 84ESCh. O - Prob. 85ESCh. O - Prob. 86ESCh. O - Prob. 87ESCh. O - Prob. 88ESCh. O - Prob. 89ESCh. O - Prob. 90ESCh. O - Prob. 91ESCh. O - Prob. 92ESCh. O - Prob. 93ESCh. O - Prob. 94ESCh. O - Prob. 95ESCh. O - Prob. 96ESCh. O - Prob. 97ESCh. O - Prob. 98ESCh. O - Prob. 99ESCh. O - Prob. 100ESCh. O - Prob. 101ESCh. O - Prob. 102ESCh. O - Prob. 103ESCh. O - Prob. 104ESCh. O - Prob. 105ESCh. O - Prob. 106ESCh. O - Prob. 107ESCh. O - Prob. 108ESCh. O - Prob. 109ESCh. O - Prob. 110ESCh. O - Prob. 111ESCh. O - Prob. 112ESCh. O - Prob. 113ESCh. O - Prob. 114ES
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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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