SLR_anova - ³ = ² + ² 1 ´ + µ , µ ∼ ¶ (0 ,U 2 )...

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Table 1: Model Summary Model R R Squared Adjusted R Square standard error of the estimate 1 u 2 u 2 u 2 u ˆ U = ± Table 2: ANOVA Model Sum of Squares df Mean Square F Sig Regression SSR 1 MSR= UU± 1 UU±/ 1 UU²/ ( ³ 2) p-value Residual SSE n-2 MSE= UU² ³ 2 = ± 2 = ˆ U 2 Total SST=SS ´ ´ n-1 Table 3: Coefficients Unstandardized Coefficients Standardized Model B Std.Error beta t Sig (Constant) ˆ ² 0 se( ˆ ² 0 ) ˆ µ 0 ¶· ( ˆ µ 0 ) p-value x ˆ ² 1 se( ˆ ² 1 ) = UU uu ˆ µ 1 ¶· ( ˆ µ 1 ) p-value The above three SPSS tables are based on a simple linear regression model
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Unformatted text preview: ³ = ² + ² 1 ´ + µ , µ ∼ ¶ (0 ,U 2 ) Note 1: degree of freedom=n-2 in all t-tests and con±dence intervals for a SLR. Note 2: ··¸ = u ( ³ ¸ − ¯ ³ ) 2 = u ³ 2 ¸ − ¹ ( ¯ ³ ) 2 ··u = u ( ˆ ³ ¸ − ¯ ³ ) 2 ··º = u ( ³ ¸ − ˆ ³ ¸ ) 2 u 2 = 1 − ··º ··¸ = ··u ··¸ ··¸ = ··u + ··º 1...
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This note was uploaded on 01/29/2012 for the course DSC 330 taught by Professor Staff during the Spring '08 term at University of Oregon.

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