SOLN_3b

1 2 so we must have e 1 e 1 e 1

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Unformatted text preview: β1 Þ This meaningless prediction results from the linearity of the regression, which can give negative results for small sales values. ˆ (1) ˆ ( 2) ˆ 5. (i) β 1 , β 1 and β 1 are all least-squares estimates based on samples from the same linear model: Yi = β0 + β1 xi + εi . ˆ (1) ˆ ( 2) ˆ So we must have E ( β 1 ) = E ( β 1 ) = E ( β 1 ) = β 1 . Also (1) ~ E ( β1 ) = n1 n2 ˆ (1) ˆ ( 2) E(β1 ) + E ( β1 ) = β1 n1 + n2 n1 + n2 ˆ (1) ˆ (2) By definition, β1 and β1 are linear estimators, so (ii) (2)  β1 = n1 n1 + n2 where w1i = (å n1 i =1 ) w1iYi + n2 n1 + n2 (å xi − x1 and w2 j = å k ( xk − x1 )2 n2...
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This homework help was uploaded on 02/16/2014 for the course ESE 302 taught by Professor Ese302 during the Fall '08 term at UPenn.

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