mba 522 9 simple regression example 1

mba 522 9 simple regression example 1 - Adjusted R Square...

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SSE SSR SST X Y X - Xbar Y - Ybar (X-Xbar)*(Y-Ybar)(X-Xbar)^2 Y hat (Y - Yhat)^2 (Yhat - Ybar)^2 (Y - Ybar)^2 2 2 -3.2 -1.4 4.48 10.24 1.77 0.0520160049 2.6506124962 1.96 4 3 -1.2 -0.4 0.48 1.44 2.79 0.0443213296 0.3727423823 0.16 5 2 -0.2 -1.4 0.28 0.04 3.30 1.6854416744 0.0103539551 1.96 7 6 1.8 2.6 4.68 3.24 4.32 2.836565097 0.8386703601 6.76 8 4 2.8 0.6 1.68 7.84 4.82 0.6799015082 2.0293751924 0.36 5.2 3.4 11.6 22.8 5.298245614 5.901754386 11.2 b1: 0.5087719298 b0: 0.7543859649 r2: 0.5269423559 r: 0.7259079528 Yhat= bo + b1 X Yhat= 0.75+0.51X (rounded values used to write the regression equation) SUMMARY OUTPUT Regression Statistics Multiple R 0.7259079528 is correlation coefficient R Square 0.5269423559 is coefficient of determination
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Unformatted text preview: Adjusted R Square 0.3692564745 (to be used with multiple linear regression) Standard Error 1.3289401308 it is calculated as: sqrt of (SSE/n-2): 1.3289401308 Observations 5 ANOVA df SS MS F Significance F Regression 1 5.9017544 5.9017544 3.3417219 0.1649922183 Residual 3 5.2982456 1.7660819 Total 4 11.2 Coefficients tandard Erro t Stat P-value Lower 95% Upper 95% Lower 95.0%Upper 95.0% Intercept 0.7543859649 1.5645215 0.4821832 0.6626685-4.2246243548 5.7333962846 -4.224624 5.7333962846 X 0.5087719298 0.2783159 1.8280377 0.1649922-0.3769542573 1.394498117 -0.376954 1.394498117...
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This note was uploaded on 11/13/2011 for the course MBA 522 taught by Professor Nabavi during the Spring '08 term at Bellevue.

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