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I'm having problems with the kinds of problems that require setting up conclusions.
Here's an example of a problem and what i did:
Tax returns include an option of designating $3 for presidential election campaigns, and it does not cost the taxpayer anything to make that designation. In a simple random sample of 250 tax returns from 1976, 27.6% of the returns designated the $3 for the campaign. In a simple random sample of 300 recent tax returns, 7.3% of the returns designated the $3 for the campaign. Use a 0.01 significance level to test the claim that the percentage of returns designating the $3 for the campaign was grerater in 1976 than it is now.
Claim: P1(1976) > P2(Now)
Opposite: P1<= P2
Null: P1=P2
Alternative: P1 >P2
P-value: 9.5506e -11 > .01
My answer: Reject Null. The sample data support the claim that the percentage of returns designating the $3 for the campaign was grerater in 1976 than it is now.
Book Answer: Reject Null. There is sufficient evidence to warrant rejection of the claim that the percentage of returns designating the $3 for the campaign was grerater in 1976 than it is now.
What I don't get is that when I am doing the conclusion the original claim has no equality so how can the book state this conclusion if there is no equality?
Last edited by van364 (2012-05-01 06:59:38)
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