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Mathematical Statistics with Resampling and R
book

Mathematical Statistics with Resampling and R

by Laura Chihara, Tim Hesterberg
September 2011
Intermediate to advanced
432 pages
11h 16m
English
Wiley
Content preview from Mathematical Statistics with Resampling and R

4

SAMPLING DISTRIBUTIONS

During an election year, pollsters may want to gauge how the voters feel about a particular issue. For instance, what proportion p of registered voters in a state intend to vote “Yes” on a referendum? From one random sample of size n = 1000, the pollsters might calculate a sample proportion of images = 0.47. However, a different sample of the same size might have yielded images = 0.49, or maybe even 0.37. To gauge the accuracy of the original estimate, we need to understand how these proportions images vary from sample to sample.

In Chapter 3, we saw examples of a permutation distribution: all possible values of a test statistic obtained by permuting the data among two groups. By comparing the observed test statistic to the null distribution, we could quantify how unusual the observed test statistic was. There are many other situations where we want to know something about how a statistic varies due to random sampling.

4.1 SAMPLING DISTRIBUTIONS

Toss a fair coin n = 10 times and note the proportion of heads images. If you repeat the experiment, you probably would not get exactly the ...

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Publisher Resources

ISBN: 9781118029855Purchase book