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Unformatted text preview: TA session 6 Econ. 103, winter 2010 Wed., Feb. 10, 2010, 10:00 a.m. and 1:00 p.m. in PP2400E. 1 Std. deviation, std. error, sampling distribution estimator’s name notation estimator’s formula expectation mean X P X i n E [ X ] = μ variance (and std.dev.) s 2 P ( X i- X ) 2 n- 1 E [ s 2 ] = σ 2 standard error se (depends on E [ se ] = σ ˆ X the estimator ˆ X ) standard error... • of the mean se X q s 2 n E [ se X ] = σ X = σ √ n • of the regression se y q P ( y i- ˆ y ) 2 n- k E [ se y ] = σ • of a model coefficient se β 1 r se 2 y P ( X i- X ) 2 E [ se β 1 ] = σ β 1 1.1 The problem Students often confuse these concepts and formulae, in particular the standard error. The meaning of “standard error” depends on context. Statistics textbooks refer both to the standard error of the regression as the “standard error” and to the standard error of a model estimate as the “standard error.” Moreover, it is already too easy to confuse the standard deviation with the standard error (Prof. Casanova helps you distinguish them on slide 18 of lecture 6). In addition it is easy to become confused between the standard deviation of a random variable and the standard deviation of a variable in a sample.random variable and the standard deviation of a variable in a sample....
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This note was uploaded on 03/15/2010 for the course ECON 103 taught by Professor Sandrablack during the Winter '07 term at UCLA.
- Winter '07