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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 noncollege graduates of the same age? Start with a null hypothesis (i.e. E [ X ] = μ X ) and test against an alternative hypothesis: 1. Twosided: i.e. E [ X ] 6 = μ X 2. Onesided: i.e. E [ x ] > μ X 1.1 Definitions pvalue: 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 tstatistic: 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
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