Thisalsomakestheuseoftheconfidenceintervalsproblematic

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Unformatted text preview: e is small. (c) xqqmath(~ weight | feed, data = chickwts, + main = "chickwt data", ylab = "Weight at six weeks (gm)") The t­distribution can also be used with a normal distribution, but the QQ­plots show that the data do not fall on a normal distribution. This also makes the use of the confidence intervals problematic because that also requires a normal distribution. 4.45 (a) ?miaa05 iidsamples <- replicate(20, sample(miaa05$PTSG, size=15, replace=TRUE)) dim(iidsamples) for(i in 1:20) { print(t.test(iidsamples[,i], conf.level=0.90)$conf.int) } [1] 1.294278 5.345722 attr(,"conf.level") [1] 0.9 [1] 3.343936 6.562731 attr(,"conf.level") [1] 0.9 [1] 2.898659 8.994674 attr(,"conf.level") [1] 0.9 [1] 2.150573 5.476093 attr(,"conf.level") [1] 0.9 [1] 4.943442 9.869891 attr(,"conf.level") [1] 0.9 [1] 2.742127 8.511206 attr(,"conf.level") [1] 0.9 [1] 3.409359 7.217308 attr(,"conf.level") [1] 0.9 [1] 2.271465 6.688535 attr(,"conf.level") [1] 0.9 [1] 4.065795 9.200872 attr(,"conf.level") [1] 0.9 [1] 2.437983 6.548684 attr(,"conf.level") [1] 0.9 [1] 3.566657 8.286676 attr(,"conf.level") [1] 0.9 [1] 3.26077 7.05923 attr(,"conf.level") [1] 0.9 [1] 3.853180 8.160153 attr(,"conf.level") [1] 0.9 [1] 3.188825 9.237841 attr(,"conf.level") [1] 0.9 [1] 2.804882 7.461784 attr(,"conf.level") [1] 0.9 [1] 4.073282 9.886718 attr(,"conf.level") [1] 0.9 [1] 3.199127 7.054206 attr(,"conf.level") [1] 0.9 [1] 2.388703 5.851297 attr(,"conf.level") [1] 0.9 [1] 4.359960 8.373373 attr(,"conf.level") [1] 0.9 [1] 2.60393 6.75607 attr(,"conf.level") [1] 0.9 (b) mean(miaa05$PTSG) = 5.593284 18 out of 20 CIs contained the true mean. (c and d) I would have expected 90% (i.e. 18 out of the CIs) to contain the true mean, so I am not surprised by the answer in (b)....
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This note was uploaded on 01/25/2014 for the course STAT 23400 taught by Professor Collins during the Winter '09 term at UChicago.

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