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Unformatted text preview: Public Health 6450 Fall 2011 Andrew Mugglin and Lynn Eberly Division of Biostatistics School of Public Health University of Minnesota ph6450@biostat.umn.edu Part 13 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Where are we going? Previously: Onesample hypothesis testing and CIs for H : = Matched Pairs hypothesis testing and CIs for H : = Twosample hypothesis testing and CIs for H : 1 2 = Errors in and power for hypothesis testing Current topic: Onesample hypothesis testing and CIs for H : p = p (Chapter 8.1) Next topic: Twosample hypothesis testing and CIs for H : p 1 p 2 = p (Chapter 8.2) Mugglin and Eberly PubH 6450 Fall 2011 Part 13 2 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Turning Point TP13a: Power and Error Review Mugglin and Eberly PubH 6450 Fall 2011 Part 13 3 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Back to the study cycle Mugglin and Eberly PubH 6450 Fall 2011 Part 13 4 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Focus on Arrow (3) for the Binomial (1) Sampling and study design get us from a population to a sample. (Done.) (2) Measurements on the sample get us statistics. (Done.) (3) Using these statistics as estimates of the population values gets us to parameters. (Done for one and twosample means: Normal and t distributions.) (4) Making a scientific statement about those parameters gets us back to the population. (Done for one and twosample means: Normal and t distributions.) Mugglin and Eberly PubH 6450 Fall 2011 Part 13 5 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Structure of CIs for onesample Normal We havent pointed it out before, but our CI formulas have a common structure: estimate (quantile)(std.error) X ( z * )( / n ) X ( t * n 1 )( s / n ) Mugglin and Eberly PubH 6450 Fall 2011 Part 13 6 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Structure of test statistics for onesample Normal Our test statistic formulas have a common structure as well: test statistic = estimate  null value std.error z = X / n t = X s / n Next well consider these same structures for the Binomial. Mugglin and Eberly PubH 6450 Fall 2011 Part 13 7 / 40 Review CIs for a Binomial Proportion CLTBased Hypothesis Testing for a Binomial Proportion Exact Hypothesis Tests for a B Revisiting the Binomial distribution When X is the total number of successes in n independent and identical Bernoulli trials with success probability...
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This note was uploaded on 11/21/2011 for the course PUBH 6450 taught by Professor Andymugglin during the Fall '10 term at Minnesota.
 Fall '10
 AndyMugglin

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