Consider the following sample of observations on coating thickness for low-viscosity paint ("Achieving a Target Value for a Manufacturing Process: A Case Study," J. Qual. Technol., 1992: 22-26): 0.83 0.88 0.88 1.04 1.09 1.12 1.29 1.31 1.48 1.49 1.59 1.62 1.65 1.71 1.76 1.83 Assume that the distribution of coating thickness is normal (a normal probability plot strongly supports this assumption) (a) Calculate a point estimate of the mean value of coating thickness, and state which estimator you used. (b) Calculate a point estimate of the median of the coating thickness distribution, and state which estimator you used. (c) Calculate a point estimate of the value that separates the largest 10% of all values in the thickness distribution from the remaining 90%, and state which estimator you used. [Hint: Express what you are trying to estimate in terms of µ and o]

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Consider the following sample of observations on coating thickness for low-viscosity
paint (“Achieving a Target Value for a Manufacturing Process: A Case Study," J.
Qual. Technol., 1992: 22-26):
0.83 0.88 0.88 1.04 1.09 1.12 1.29 1.31
1.48 1.49 1.59 1.62 1.65 1.71 1.76 1.83
Assume that the distribution of coating thickness is normal (a normal probability plot
strongly supports this assumption)
(a) Calculate a point estimate of the mean value of coating thickness, and state which
estimator you used.
(b) Calculate a point estimate of the median of the coating thickness distribution,
and state which estimator you used.
(c) Calculate a point estimate of the value that separates the largest 10% of all values
in the thickness distribution from the remaining 90%, and state which estimator
you used. [Hint: Express what you are trying to estimate in terms of u and o]
(d) Estimate Pr(X < 1.5), i.e., the proportion all thickness values less than 1.5. [Hint:
If you knew the values of u and o, you could calculate this probability. These
values are not available, but they can be estimated.]
Transcribed Image Text:Consider the following sample of observations on coating thickness for low-viscosity paint (“Achieving a Target Value for a Manufacturing Process: A Case Study," J. Qual. Technol., 1992: 22-26): 0.83 0.88 0.88 1.04 1.09 1.12 1.29 1.31 1.48 1.49 1.59 1.62 1.65 1.71 1.76 1.83 Assume that the distribution of coating thickness is normal (a normal probability plot strongly supports this assumption) (a) Calculate a point estimate of the mean value of coating thickness, and state which estimator you used. (b) Calculate a point estimate of the median of the coating thickness distribution, and state which estimator you used. (c) Calculate a point estimate of the value that separates the largest 10% of all values in the thickness distribution from the remaining 90%, and state which estimator you used. [Hint: Express what you are trying to estimate in terms of u and o] (d) Estimate Pr(X < 1.5), i.e., the proportion all thickness values less than 1.5. [Hint: If you knew the values of u and o, you could calculate this probability. These values are not available, but they can be estimated.]
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