Lab1 Solutions - # 4. attach(nym.2002) # a. hist(age) # it...

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Lab #1 Solutions # 1. gas <- scan() 65311 65624 65908 66219 66499 66821 67145 67477 diff(gas) # this gives the miles driven between fill-ups. mean(gas) # this is the average reading on the odomater during the study period. mean(diff(gas)) # this is the average miles driven between fill-ups. # 2. # a. length(treering) # b. min(treering) # c. max(treering) # d. sum(treering > 1.5) # 3. # a. sum(rivers > 500)/length(rivers) # b. sum(rivers < mean(rivers))/length(rivers) # c. fivenum(rivers) # d. IQR(rivers) # e. boxplot(rivers) # there appear to be a number of outliers above 1.5*iqr above q3.
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Unformatted text preview: # 4. attach(nym.2002) # a. hist(age) # it looks unimodal with possibly a fat right tail. # b. hist(time, breaks=10) hist(time, breaks=20) hist(time, breaks=30) # the one with 20 breaks looks more sensible than the one with 10 breaks bc it has smaller groups, but the one with 30 breaks isn't as smooth as the one with 20. hist(time, breaks=20, prob=TRUE) lines(density(time)) # c. boxplot(time) # right-skewed, for sure. Lab #1 Solutions...
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This note was uploaded on 04/27/2009 for the course STAT 200 taught by Professor Agniel during the Spring '09 term at University of Illinois at Urbana–Champaign.

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Lab1 Solutions - # 4. attach(nym.2002) # a. hist(age) # it...

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