Explanation Calculation: There 6693 spam messages received by different departments. The total number of spam messages received by the departments that are mentioned in the provided data table is calculated as: ( Total number of spam messages received by the mentioned departments ) = ( 1818 + ⋯ + 103 ) = 6067 The number of spam messages received by the other department can be obtained by subtracting the sum of all the counts of mentioned department from the total number of spam messages received. Therefore, the spam messages received by the other department can be calculated as: ( Total number of spam messages ) − ( Total number of spam messages re

LaunchPad for Moore's Introduction to the Practice of Statistics (12 month access)

8th Edition

ISBN: 9781464133404

Author: David S. Moore, George P. McCabe, Bruce A. Craig

Publisher: W. H. Freeman

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Chapter 1, Problem 168E

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To find: The total number of spam messages received by other departments whose spam message count is less than 100 and a graph for the provided data set.

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Chapter 1, Problem 167E

Chapter 1, Problem 169E

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(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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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?…

Chapter 1 Solutions

LaunchPad for Moore's Introduction to the Practice of Statistics (12 month access)

Ch. 1.1 - Prob. 1UYK Ch. 1.1 - Prob. 2UYK Ch. 1.1 - Prob. 3UYK Ch. 1.1 - Prob. 5UYK Ch. 1.1 - Prob. 4UYK Ch. 1.1 - Prob. 6UYK Ch. 1.1 - Prob. 7UYK Ch. 1.1 - Prob. 8E Ch. 1.1 - Prob. 9E Ch. 1.1 - Prob. 10E

Ch. 1.1 - Prob. 11E Ch. 1.1 - Prob. 12E Ch. 1.1 - Prob. 13E Ch. 1.1 - Prob. 14E Ch. 1.1 - Prob. 15E Ch. 1.2 - Prob. 16UYK Ch. 1.2 - Prob. 17UYK Ch. 1.2 - Prob. 18UYK Ch. 1.2 - Prob. 19UYK Ch. 1.2 - Prob. 20UYK Ch. 1.2 - Prob. 21UYK Ch. 1.2 - Prob. 22UYK Ch. 1.2 - Prob. 23UYK Ch. 1.2 - Prob. 24UYK Ch. 1.2 - Prob. 25E Ch. 1.2 - Prob. 26E Ch. 1.2 - Prob. 27E Ch. 1.2 - Prob. 28E Ch. 1.2 - Prob. 29E Ch. 1.2 - Prob. 30E Ch. 1.2 - Prob. 31E Ch. 1.2 - Prob. 32E Ch. 1.2 - Prob. 33E Ch. 1.2 - Prob. 34E Ch. 1.2 - Prob. 35E Ch. 1.2 - Prob. 42E Ch. 1.2 - Prob. 36E Ch. 1.2 - Prob. 43E Ch. 1.2 - Prob. 37E Ch. 1.2 - Prob. 44E Ch. 1.2 - Prob. 38E Ch. 1.2 - Prob. 45E Ch. 1.2 - Prob. 46E Ch. 1.2 - Prob. 39E Ch. 1.2 - Prob. 40E Ch. 1.2 - Prob. 41E Ch. 1.3 - Prob. 47UYK Ch. 1.3 - Prob. 48UYK Ch. 1.3 - Prob. 49UYK Ch. 1.3 - Prob. 50UYK Ch. 1.3 - Prob. 51UYK Ch. 1.3 - Prob. 52UYK Ch. 1.3 - Prob. 53UYK Ch. 1.3 - Prob. 54UYK Ch. 1.3 - Prob. 55UYK Ch. 1.3 - Prob. 56UYK Ch. 1.3 - Prob. 57UYK Ch. 1.3 - Prob. 58UYK Ch. 1.3 - Prob. 59UYK Ch. 1.3 - Prob. 60UYK Ch. 1.3 - Prob. 67E Ch. 1.3 - Prob. 69E Ch. 1.3 - Prob. 61E Ch. 1.3 - Prob. 62E Ch. 1.3 - Prob. 63E Ch. 1.3 - Prob. 64E Ch. 1.3 - Prob. 65E Ch. 1.3 - Prob. 66E Ch. 1.3 - Prob. 74E Ch. 1.3 - Prob. 75E Ch. 1.3 - Prob. 76E Ch. 1.3 - Prob. 71E Ch. 1.3 - Prob. 68E Ch. 1.3 - Prob. 70E Ch. 1.3 - Prob. 77E Ch. 1.3 - Prob. 78E Ch. 1.3 - Prob. 79E Ch. 1.3 - Prob. 80E Ch. 1.3 - Prob. 81E Ch. 1.3 - Prob. 82E Ch. 1.3 - Prob. 83E Ch. 1.3 - Prob. 84E Ch. 1.3 - Prob. 85E Ch. 1.3 - Prob. 86E Ch. 1.3 - Prob. 87E Ch. 1.3 - Prob. 88E Ch. 1.3 - Prob. 89E Ch. 1.3 - Prob. 90E Ch. 1.3 - Prob. 91E Ch. 1.3 - Prob. 92E Ch. 1.3 - Prob. 93E Ch. 1.3 - Prob. 94E Ch. 1.3 - Prob. 95E Ch. 1.3 - Prob. 96E Ch. 1.3 - Prob. 72E Ch. 1.3 - Prob. 97E Ch. 1.3 - Prob. 98E Ch. 1.3 - Prob. 99E Ch. 1.3 - Prob. 100E Ch. 1.3 - Prob. 73E Ch. 1.4 - Prob. 101UYK Ch. 1.4 - Prob. 102UYK Ch. 1.4 - Prob. 103UYK Ch. 1.4 - Prob. 104UYK Ch. 1.4 - Prob. 105UYK Ch. 1.4 - Prob. 106UYK Ch. 1.4 - Prob. 107UYK Ch. 1.4 - Prob. 108UYK Ch. 1.4 - Prob. 109E Ch. 1.4 - Prob. 110E Ch. 1.4 - Prob. 111E Ch. 1.4 - Prob. 112E Ch. 1.4 - Prob. 113E Ch. 1.4 - Prob. 114E Ch. 1.4 - Prob. 115E Ch. 1.4 - Prob. 116E Ch. 1.4 - Prob. 117E Ch. 1.4 - Prob. 118E Ch. 1.4 - Prob. 119E Ch. 1.4 - Prob. 120E Ch. 1.4 - Prob. 121E Ch. 1.4 - Prob. 122E Ch. 1.4 - Prob. 123E Ch. 1.4 - Prob. 124E Ch. 1.4 - Prob. 125E Ch. 1.4 - Prob. 126E Ch. 1.4 - Prob. 127E Ch. 1.4 - Prob. 128E Ch. 1.4 - Prob. 129E Ch. 1.4 - Prob. 130E Ch. 1.4 - Prob. 131E Ch. 1.4 - Prob. 132E Ch. 1.4 - Prob. 133E Ch. 1.4 - Prob. 134E Ch. 1.4 - Prob. 135E Ch. 1.4 - Prob. 136E Ch. 1.4 - Prob. 137E Ch. 1.4 - Prob. 138E Ch. 1.4 - Prob. 139E Ch. 1.4 - Prob. 140E Ch. 1.4 - Prob. 141E Ch. 1.4 - Prob. 142E Ch. 1.4 - Prob. 143E Ch. 1.4 - Prob. 144E Ch. 1.4 - Prob. 145E Ch. 1.4 - Prob. 146E Ch. 1.4 - Prob. 147E Ch. 1.4 - Prob. 148E Ch. 1.4 - Prob. 149E Ch. 1.4 - Prob. 150E Ch. 1.4 - Prob. 151E Ch. 1.4 - Prob. 152E Ch. 1.4 - Prob. 153E Ch. 1.4 - Prob. 154E Ch. 1.4 - Prob. 155E Ch. 1 - Prob. 156E Ch. 1 - Prob. 157E Ch. 1 - Prob. 158E Ch. 1 - Prob. 159E Ch. 1 - Prob. 160E Ch. 1 - Prob. 161E Ch. 1 - Prob. 162E Ch. 1 - Prob. 163E Ch. 1 - Prob. 164E Ch. 1 - Prob. 165E Ch. 1 - Prob. 166E Ch. 1 - Prob. 167E Ch. 1 - Prob. 168E Ch. 1 - Prob. 169E Ch. 1 - Prob. 170E Ch. 1 - Prob. 171E Ch. 1 - Prob. 172E Ch. 1 - Prob. 173E Ch. 1 - Prob. 174E Ch. 1 - Prob. 175E Ch. 1 - Prob. 176E Ch. 1 - Prob. 177E

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The total number of spam messages received by other departments whose spam message count is less than 100 and a graph for the provided data set.

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To find: The total number of spam messages received by other departments whose spam message count is less than 100 and a graph for the provided data set.

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Chapter 1 Solutions