Formula Sheet - That is for EACH of them to get t you have...

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Slope and y-Intercept for the Estimated Regression Equation Coefficient of Determination: SST SSR r = 2 Adjusted 2 r ( 29 1 1 1 - + - - = n SST k n SSE Sum of Squares Due to Error Total Sum of Squares 2 - Σ = i i y y SSE ( 29 2 y y SST i - Σ = Sum of Squares Due to Regression Relationship among SST, SSR and SST 2 - Σ = y y SSR i SSE SSR SST + = Computational Formula for SSR Computational Formula for SST ( 29 [ ] ( 29 n x x n y x y x SSR i i i i i i 2 2 2 / Σ - Σ Σ Σ - Σ = SST = ( 29 n y y i i 2 2 Σ - Σ Mean Square Error Standard Error of the Estimate Estimated Standard Deviation of 1 b 2 2 - = = n SSE MSE s 2 - = = n SSE MSE s ( 29 n x x s s i i b / 2 2 1 Σ - Σ = t Test Statistic F Test Statistic 1 1 b s b t = MSE MSR F = For Multiple Regression: The Model: ε β + + + + + = k k x x x y 2 2 1 1 0 Coefficient of Determination: SST SSR r = 2 bi i i s b t = Where i stands for each of the independent variables.
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Unformatted text preview: That is, for EACH of them, to get t, you have to divide by the Standard deviation of THAT independent variable. ( 29 ( 29 x b y b n x x n y x y x b i i i i i i 1 2 2 1 / /-= Σ-Σ Σ Σ-Σ = ) 1 ( +-= = k n SSR k SSR MSE MSR F Where k of the number of the Independent variables. ( 29 1 +-= = k n SSE MSE s where k is the number of independent variables Mean Square Due to Regression: MSR = SSR/Number of independent variables = SSR / k Multiple Regression Model: ε β + + + + = k k x x x y 2 2 1 1...
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Formula Sheet - That is for EACH of them to get t you have...

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