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mba 522 21 detailed Excel solution to PPT from ch 12

mba 522 21 detailed Excel solution to PPT from ch 12 - 1...

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Detailed Solution to the example in chapter 12 PPT file SUMMARY OUTPUT Regression Statistics Multiple R 0.9365858 R Square 0.877193 Adjusted R Square0.8362573 Standard Error 2.1602469 Observations 5 ANOVA df SS MS F Significance F Regression 1 100 100 21.428571 0.0189862 Residual 3 14 4.6666667 Total 4 114 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95%Lower 95.0% Upper 95.0% Intercept 10 2.3664319 4.2257713 0.024236 2.4689575 17.531042 2.4689575 17.531042 5 1.0801234 4.6291005 0.0189862 1.5625651 8.4374349 1.5625651 8.4374349 RESIDUAL OUTPUT Observation Residuals ndard Residuals 1 15 -1 -0.534522 2 25 -1 -0.534522 3 20 -2 -1.069045 4 15 2 1.069045 5 25 2 1.069045 Ads (X) Predicted Cars sold (Y) 0.5 1 1.5 2 2.5 3 3.5 -5 5 Ads (X) Residual Plot Ads (X) Residuals

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Detailed Calculations for the example in PPT slides from ch. 12 SSR SSE SST X - Xbar Y - Ybar (Yhat-Ybar)^2 (Y-Yhat)^2 (Y-Ybar)^2 1
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Unformatted text preview: 1 14-1-6 6 1 15 25 1 36 3 24 1 4 4 1 25 25 1 16 2 18-2-0 20 4 4 1 17-1-3 3 1 15 25 4 9 3 27 1 7 7 1 25 25 4 49 sum 10 100 20 4 100 14 114 mean 2 20 SST = SSR + SSE = 100+14 = 114 compute regression line: b1 (slope) = summation of column F divided by summation of column G = 20/4 = 5 b1 = 5 bo (intercept) = Ybar - b1 (X bar) = 20 - 5(2) = 10 bo = 10 So, the regression line which estimates Y is: Yhat = 10 + 5X Compute Coefficient of Determination (r-squared) r^2 = SSR / SST = 100/114 = 0.8772 r^2 = 0.8772 Compute Correlation Coefficient (r ) take square root of coefficient of determination r = 0.9366 Ads (X) Cars sold (Y) multiply Col D & E Col D squared Predicted Y (Y hat) 0.5 1 1.5 2 2.5 3 3.5 5 10 15 20 25 30 Cars sold (Y) Ads Cars Sold...
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mba 522 21 detailed Excel solution to PPT from ch 12 - 1...

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