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MCS,PHD
Argosy University/ Phoniex University/
Nov-2005 - Oct-2011
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Phoniex University
Oct-2001 - Nov-2016
47.    Bread Volume We can often use an analysis of variance with data that are not well matched to a linear regression, using dummy variables to avoid the need to model a curved relationship. In this example, a bakery ran an experiment to measure the effect of different recipes on the volume of commercial bread loaves (measured in milliliters). Customers gener- ally prefer larger-looking loaves rather than smaller loaves, even if the net weights are the same. In this experiment, the bakery varied the level of potassium bromate from 0 to 4 milligrams.16
(a)Â Â Consider the linear regression of loaf volume on the amount of potassium bromate. Do the data appear to meet the assumptions required by the simple regression model?
(b)Â Â Do the data appear to meet the conditions required for fitting an ANOVA? (Determine this visually, before fitting the ANOVA.)
(c)Â Â Â Fit an ANOVA by treating the amounts of potas- sium bromate as identifying five categories. Can the bakery affect the volume of its loaves by vary- ing the amount of this ingredient?
(d)Â Â If your software provides it, give the 95% Tukey confidence interval for the difference in volume produced with no potassium bromate and 2 mil- ligrams of potassium bromate. Is there a statisti- cally significant difference in volume?
(e)Â Â Â Give the 95% Bonferroni confidence interval for the difference in volume produced with no potassium bromate and 2 milligrams of potas- sium bromate. Is there a statistically significant difference in volume?
(f)Â Â Â Explain any differences between the conclu- sions of part (c) and the conclusions of parts
(d)Â Â and (e).
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