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Unformatted text preview: 142) Model the percentage drop in light output as roughly a linear function of the length of operation, plus an adjustment for whether the surface is clean or dirty. SUMMARY OUTPUT Regression Statistics Multiple R 0.92 R Square 0.86 Adjusted R 0.83 Standard E 5.39 Observatio 14 ANOVA df SS MS F Sig F Regression 2 1886.66 943.33 32.46 Residual 11 319.7 29.06 Total 13 2206.36 Coefficients Standard Error t Stat Pvalue Lower 95% Intercept4.48 2.971.51 0.1611.02 Surface 17.29 2.88 6 10.94 Length 0.01 5.38 0.01 i) Estimate the model Y = B0 + B1x1 + B2x2 + E Y = 4.48 17.29x1 +.0097x2 + E ii) How well does this model fit the data? The model fits the data well since r^2 = .86 iii) Consider a dirty light bulb which has been in use for a thousand hours. Is there H0 Y = 4.48 17.29(0) +.0097(1000) H1 B < 8 Y = 5.22 h 0.15 Su 2.07 T1.34 p 0.1 No, there is not evidence iv Test the following hypothesis at the 5% significance level: The expected drop in output for a clean bulb is about fifteen percentage points greater than that of that of a dirty light bulb in use for the same amount of time. sig level 0.05 H0 B = 15 T Stat 0.79 H1 B =/ 15 T Test 0.03 evidence that the expected drop in output for this bulb is less than 8%? h = 0.1473 B > 8 Reject null 141 We model the sale price of a building as roughly a linear function of its age, number of apartments, and gross area, with an adjustment for the condition of SUMMARY OUTPUT Regression Statistics Multiple R 0.99 R Square 0.98 Adjusted R Square 0.98 Standard Error 30094.75 Observations 25 ANOVA df SS MS F ignificance F Regression 5 ### ### 233.34 Residual 19 ### ### Total 24 ### Coefficientstandard Err t Stat Pvalue Lower 95% Intercept 114463.93 24010.4824010....
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 Fall '10
 BHATIA
 Marketing

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