{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}


Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: taken together. The “much worse” proportions in Example 23.1 are significantly different (P=0.0047) if we compare just this one outcome; but, is it surprising that the most different proportions among five outcomes differ by this much? The problem of how to do many comparisons at once with an overall measure of confidence in all our conclusions is a common one in statistics. This is the problem of multiple comparisons. Statistical methods for dealing with multiple comparisons usually have two steps: 1 An overall test to see if there is good evidence of any differences among the parameters that we want to compare. 2 A detailed follow up analysis to decide which of the parameters differ and to estimate how large the differences are. The overall test is often reasonably straightforward. The follow up analysis can be quite elaborate. We will concentrate on the overall test, along with data analysis that points to the nature of the differences. 3 M316 Chapter 23 Dr. Berg Exercise (23.4) Who’s Online A sample survey by the Pew Internet and American Life Project asked a random sample of adults about use of the Internet and about the type of community they lived in. Here is the two‐way table: Rural Suburban Urban Internet Users 433 1072 536 Nonusers 463 627 388 a) Give a 95% confidence interval, for percents of adults in rural, suburban, and urban communities who use the Internet. b) Explain clearly why we are not 95% confident that all three of these intervals capture their respective population proportions. Expected Counts in Two‐Way Tables Our general null hypothes...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online