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Unformatted text preview: Joe Kuehn ( [email protected] ) Econ 103 Sec. 1A/1B UCLA Fall 2010 1 Hypothesis testing A hypothesis can be a yes or no question, i.e are the mean earning for college graduates equal to $25/hr or are mean earnings for college graduates equal to that of non-college graduates of the same age? Start with a null hypothesis (i.e. E [ X ] = μ X ) and test against an alternative hypothesis: 1. Two-sided: i.e. E [ X ] 6 = μ X 2. One-sided: i.e. E [ x ] > μ X 1.1 Definitions p-value: probability of drawing a statistic at least as adverse to the null hypothesis as the one you actually computed in sample, assuming that the null hypothesis is correct sample variance: s X 2 = 1 n- 1 * ∑ n i =1 ( X i-- X ) 2 Standard error of- X : an estimator of the standard deviation of- X SE [- X ] = s X √ n t-statistic: t =- X- μ X SE [- X ] Type I error: the null hypothesis is rejected when it is really true Type II error: the null hypothesis is not rejected when it is false significance level: the prespecified probability of rejected of a hypothesis test when the hypothesis is true i.e. the prespecified probability of a type I error critical value: the value of the test statistic for which the test just rejects the null hypothesis at the given...
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This note was uploaded on 10/23/2010 for the course STAT 10 taught by Professor Davis during the Spring '10 term at UCLA.
- Spring '10