lab9 - # In this Lab, we'll do t.tests and chi-squared...

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# In this Lab, we'll do t.tests and chi-squared tests. It seems long, but # it's only 20 lines of code, many of which are very similar. So, use # the UP/DOWN arrows. # 1) Even though we've used t.test() already, we're going to use it again, # but this time with emphasis on the resulting p-values, as opposed to the CI. # The reason is that t.test() has some arguments that do not affect the CI, but # do affect the p-value. # Exercise 8.38 is good to do, because it asks if some difference exceeds # some number. # # First, copy/paste the data from # http://www.stat.washington.edu/marzban/390/8_38_dat.txt . # # Then, ask yourself if the data are paired. # In this problem the answer is Yes! weight=c(14.6,14.4,19.5,24.3,16.3,22.1,23,18.7,19,17,19.1,19.6,23.2,18.5,15.9) tread=c(11.3,5.3,9.1,15.2,10.1,19.6,20.8,10.3,10.3,2.6,16.6,22.4,23.6,12.6,4.4) # Before doing any test, "look" at the data: boxplot(weight,tread,names=c("weight","treadmill")) # Discuss these relative boxplots. But, keep in mind that the data are # paired; these boxplots do NOT reflect that fact. As such, # the comparison of these boxplots may be misleading. # The scatterplot of the two variables, shows a correlation, # confirming that the data are paired. plot(weight,tread) cor(weight,tread) # Now, the t.test assumes that the population is normal. So, let's see # if our data are at least consistent with that assumption: qqnorm(weight) qqnorm(tread) # These could look better; but with the small sample size we're # dealing with, they are normal enough. Also, technically, since # we need to do a paired test, it is the differences which should have
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lab9 - # In this Lab, we'll do t.tests and chi-squared...

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