SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Sq Standard Error Observations 0.9027 0.8148 0.8016 148.1904 16 ANOVA F Signdicance F SS 1 1,352,635.29 1,352,635.29 14 307 445.65 df MS Regression Residual 61.59 0.00000171 21,960.40 Total 15 1,660,080.94 Lower 95% Coefficients Standard Eror 437.8799 16.9448 : Stat 5.3175 7.8482 Pvalue 0.0001 0.0000 Upper 95% Lower 95.0% Upper 95.0% 614.4981 21.5756 82.3477 261.2617 614.4981 261.2617 Intercept $TV ads 2.1591 12.3141 21.5756 12.3141
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Do we reject the hypothesis of the attached file?
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- The processing of raw coal involves “washing,” in which coal ash (nonorganic, incombustible material) is removed. The article “Quantifying Sampling Precision for Coal Ash Using Gy’s Discrete Model of the Fundamental Error” (Journal of Coal Quality, 1989:33–39) provides data relating the percentage of ash to the volume of a coal particle. The average percentage of ash for six volumes of coal particles was measured. The data are as follows: Volume (cm3) 0.01 0.06 0.58 2.24 15.55 276.02 Percent ash 3.32 4.05 5.69 7.06 8.17 9.36 Using the most appropriate model, construct a 95% confidence interval for the mean percent ash for particles with a volume of 50 cm3. Round the answers to three decimal places. The 95% confidence interval is , .The processing of raw coal involves “washing,” in which coal ash (nonorganic, incombustible material) is removed. The article “Quantifying Sampling Precision for Coal Ash Using Gy’s Discrete Model of the Fundamental Error” (Journal of Coal Quality, 1989:33–39) provides data relating the percentage of ash to the volume of a coal particle. The average percentage of ash for six volumes of coal particles was measured. The data are as follows: Volume (cm3) 0.01 0.06 0.58 2.24 15.55 276.02 Percent ash 3.32 4.05 5.69 7.06 8.17 9.36 Using the most appropriate model, predict the percent ash for particles with a volume of 48 cm3. Round answer to three decimal places.The supervisor of an orange juice-bottling company is considering the purchase of a new machine to bottle 16-fluid-ounce (473-milliliter) bottles of 100% pure orange juice and wants an estimate of the difference in the mean filling weights between the new machine and the old machine. Random samples of bottles of orange juice that had been filled by both machines were obtained. Estimate the difference in the mean filling weights between the new and the old machines? Dis- cuss the assumptions. Use α = 0.10. New Machine Old MachineMean 470 milliliters 460 millilitersStandard deviation 5 milliliters 7 millilitersSample size 15 12
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