The five most common words appearing in spam emails are shipping! today! here! available, and fingertips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17, 2010). Many spam filters separate spam from ham (email not considered to be spam) through the application of the Bayes theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below. shipping! .051 today! .045 here! .034 available .014 fingertips! .014 Also suppose that the proportions of ham messages that have these words are shipping! .0015 today! .0022 here .0022 available .0041 fingertips! .0011 If a message includes the word shipping! what is the probability the message is spam? If a message includes the word shipping!, what is the probability the message is ham? Should messages that include the word shipping! be flagged as spam? If a message includes the word today!, What is the probability the message is spam? If a message includes the word here, "what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? Why? If a message includes the \vord available,\vhat is the probability the message is spam? If a message includes the word fingertips! what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? why? .What insights do the results of parts (b) and (c) yield about what enables spam filter that uses Bayes' theorem to work effectively? The five mostcommon \vords appearingin spam emails are shippi11g!, today!,here!,a•01ilable,and finge 1tips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17,2010).Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes'theorem. Suppose that for one email account, 1 in every 10 messages is spam andthe proportions of spam messages that havethe five most common \vords in spam email are given belo\v. sl1ippi11g! .051 today! .045 he1·e! .034 atrailable .014 finge1·tips! .014 Also suppose thatthe proportions of ham messages that havethese \vords are

The five most common words appearing in spam emails are shipping! today! here! available, and fingertips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17, 2010). Many spam filters separate spam from ham (email not considered to be spam) through the application of the Bayes theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below. shipping! .051 today! .045 here! .034 available .014 fingertips! .014 Also suppose that the proportions of ham messages that have these words are shipping! .0015 today! .0022 here .0022 available .0041 fingertips! .0011 If a message includes the word shipping! what is the probability the message is spam? If a message includes the word shipping!, what is the probability the message is ham? Should messages that include the word shipping! be flagged as spam? If a message includes the word today!, What is the probability the message is spam? If a message includes the word here, "what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? Why? If a message includes the \vord available,\vhat is the probability the message is spam? If a message includes the word fingertips! what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? why? .What insights do the results of parts (b) and (c) yield about what enables spam filter that uses Bayes' theorem to work effectively? The five mostcommon \vords appearingin spam emails are shippi11g!, today!,here!,a•01ilable,and finge 1tips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17,2010).Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes'theorem. Suppose that for one email account, 1 in every 10 messages is spam andthe proportions of spam messages that havethe five most common \vords in spam email are given belo\v. sl1ippi11g! .051 today! .045 he1·e! .034 atrailable .014 finge1·tips! .014 Also suppose thatthe proportions of ham messages that havethese \vords are

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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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The five most common words appearing in spam emails are shipping! today! here! available, and fingertips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17, 2010). Many spam filters separate spam from ham (email not considered to be spam) through the application of the Bayes theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below.

shipping!	.051
today!	.045
here!	.034
available	.014
fingertips!	.014

Also suppose that the proportions of ham messages that have these words are

shipping!	.0015
today!	.0022
here	.0022
available	.0041
fingertips!	.0011

If a message includes the word shipping! what is the probability the message is spam? If a message includes the word shipping!, what is the probability the message is ham? Should messages that include the word shipping! be flagged as spam?
If a message includes the word today!, What is the probability the message is spam? If a message includes the word here, "what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? Why?
If a message includes the \vord available,\vhat is the probability the message is spam? If a message includes the word fingertips! what is the probability the message is spam? Which of these two words is a stronger indicator that a message is a spam? why?
.What insights do the results of parts (b) and (c) yield about what enables spam filter that uses Bayes' theorem to work effectively?

The five mostcommon \vords appearingin spam emails are shippi11g!, today!,here!,a•01ilable,and finge 1tips! (Andy Greenberg, "The Most Common Words In Spam Email;' Fo1beswebsite, March 17,2010).Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes'theorem. Suppose that for one email account, 1 in every 10 messages is spam andthe proportions of spam messages that havethe five most common \vords in spam email are given belo\v.

sl1ippi11g!	.051
today!	.045
he1·e!	.034
atrailable	.014
finge1·tips!	.014

Also suppose thatthe proportions of ham messages that havethese

\vords are

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