This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Joe Kuehn ( email@example.com ) 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 ] = X ) and test against an alternative hypothesis: 1. Two-sided: i.e. E [ X ] 6 = X 2. One-sided: i.e. E [ x ] > 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- 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...
View Full Document
- Spring '10