A edugen.wileyplus.comWileyPLUSFinal ProjectPLUSLock, Statistics: Unlocking the Power of Data, 2eHelp | System Announcements4 ВАСКNEXTPRINTER VERSIONChapter P, Section 2, Exercise 062Identifying Spam Text MessagesBayes' rule can be used to identify and filter spam emails and text messages. This question refers to a large collection of real SMS text messages from participating cellphone users.' In this collection,747 of the 5574 total messages (13.40%) are identified as spam. The word "free" is contained in 4.75% of all messages, and 3.57% of all messages both contain the word "free" and are marked asspam. The word "text" (or "txt") is contained in 7.01% of all messages, and in 38.55% of all spam messages. Of all spam messages, 17.00% contain both the word "free" and the word "text" (or"txt"). For example, "Congrats!! You are selected to receive a free camera phone, txt ******* to claim your prize." Of all non-spam messages, 0.06% contain both the word "free" and the word"text" (or "txt").1tion 1,tion 1,%3Dtion 1,tion 1,Given that a message contains the word "free" but does NOT contain the word "text" (or "txt"), what is the probability that it is spam?tion 1,Round your answer to three decimal places.tion 1,P(Spam if Free and not Text) =%3Dtion 1,tion 1,'Almeida, T., Hidalgo, J., Yamakami, A., "Contributions to the Study of SMS Spam Filtering: New Collection and Results," Proceedings of the 2011 ACM Symposium on Document Engineering(DOCENG'11), Association for Computing Machinery, Mountain View, CA, 2011.%3Dtion 1,ion 1,the absolute tolerance is +/-0.001Eion 1,Click if you would like to Show Work for this question: Open Show Workion 1,ion 2,By accessing this Question Assistance, you will learn while you earn points based on the Point Potential Policy set by your instructor.ion 2,Question Attempts: UnlimitedSAVE FOR LATERSUBMIT ANSWERion 2,Earn Maximum Points available only if youanswer this question correctly in fourattempts or less.Fion 2,0%cion 2,ion 2,zion 2,powered byMapleNettion 2,tion 3,tion 3,

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Description: A edugen.wileyplus.comWileyPLUSFinal ProjectPLUSLock, Statistics: Unlocking the Power of Data, 2eHelp | System Announcements4 ВАСКNEXTPRINTER VERSIONChapter P, Section 2, Exercise 062Identifying Spam Text MessagesBayes' rule can be used to identify

GivenP(spam)=13.4100=0.134P(not spam)=1-P(spam)=0.866P(free)=4.75100=0.0475P(text∩free/spam)=17100=0.17P(text∩free/not spam)=0.06100=0.0006 P(free/spam)=P(free∩spam)P(spam)=0.03570.134=0.2664 By using Total Probability Theorem P(free∩text)=P(spam)×(text∩free/spam)+P(not spam)×P(text∩free/not spam) =0.134×0.17 + 0.866×0.0006 =0.0233 P(free∩not text)=P(free)-P(free∩text) =0.0475-0.0233 =0.0242P(spam/free∩not text)=P(spam)×(free∩not text/spam)P(free∩not text) =0.134×0.09640.0242 =0.0964Therefore , the rquired probability is:P(spam/free∩not text)=P(spam)×P(free∩not text/spam)P(free∩not text) =0.134×0.09640.0242 =0.534 The required probability is P(spam/free∩not text)=0.534

Math

Statistics

Identifying Spam Text Messages Bayes' rule can be used to ldentify and filter spam emails and text messages. This question refers to a large collection of real SMS text messages from participating cellphone users.' In this collection, 747 of the 5574 total messages (13.40%) are identified as spam. The word "free" is contained in 4.75% of all messages, and 3.57% of all messages both contain the word "free" and are marked as spam. The word "text" (or "txt") is contained in 7.01% of all messages, and in 38.55% of all spam messages. Of all spam messages, 17.00% contain both the word "free" and the word "text" (or "txt"). For example, "Congrats!! You are selected to receive a free camera phone, txt ******* to claim your prize." Of all non-spam messages, 0.06% contain both the word "free" and the word "text" (or "txt"). Given that a message contains the word "free" but does NOT contain the word "text" (or "txt"), what is the probability that it is spam? Round your answer to three decimal places. P(Spam if Free and not Text) =

Identifying Spam Text Messages Bayes' rule can be used to ldentify and filter spam emails and text messages. This question refers to a large collection of real SMS text messages from participating cellphone users.' In this collection, 747 of the 5574 total messages (13.40%) are identified as spam. The word "free" is contained in 4.75% of all messages, and 3.57% of all messages both contain the word "free" and are marked as spam. The word "text" (or "txt") is contained in 7.01% of all messages, and in 38.55% of all spam messages. Of all spam messages, 17.00% contain both the word "free" and the word "text" (or "txt"). For example, "Congrats!! You are selected to receive a free camera phone, txt ******* to claim your prize." Of all non-spam messages, 0.06% contain both the word "free" and the word "text" (or "txt"). Given that a message contains the word "free" but does NOT contain the word "text" (or "txt"), what is the probability that it is spam? Round your answer to three decimal places. P(Spam if Free and not Text) =

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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