Chapter 16: Q28E (page 700)
Construct a control chart for the data of Exercise 25 by using the transformation suggested in the text.
Short Answer
The process is in-control.
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Chapter 16: Q28E (page 700)
Construct a control chart for the data of Exercise 25 by using the transformation suggested in the text.
The process is in-control.
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A manufacturer of dustless chalk instituted a quality control program to monitor chalk density. The sample standard deviations of densities for\(24\)different subgroups, each consisting of\(n = 8\)chalk specimens, were as follows:
\(\begin{aligned}{*{20}{c}}{.204}&{.315}&{.096}&{.184}&{.230}&{.212}&{.322}&{.287}\\{.145}&{.211}&{.053}&{.145}&{.272}&{.351}&{.159}&{.214}\\{.388}&{.187}&{.150}&{.229}&{.276}&{.118}&{.091}&{.056}\end{aligned}\)
Calculate limits for an\(S\)chart, construct the chart, and check for out-of-control points. If there is an out-of-control point, delete it and repeat the process.
When\({S^2}i\)s the sample variance of a normal random sample,\((n - 1){S^2}/{\sigma ^2}\)has a chi-squared distribution with\(n - 1df\), so
\(P\left( {\chi _{.999,n - 1}^2 < \frac{{(n - 1){S^2}}}{{{\sigma ^2}}} < \chi _{.001,n - 1}^2} \right) = .998\)
from which
\(P\left( {\frac{{{\sigma ^2}\chi _{.999,n - 1}^2}}{{n - 1}} < {S^2} < \frac{{{\sigma ^2}\chi _{.001,n - 1}^2}}{{n - 1}}} \right) = .998\)
This suggests that an alternative chart for controlling process variation involves plotting the sample variances and using the control limits
\(\begin{aligned}{l}LCL = \overline {{s^2}} {\chi _{ - 999_{ - n - 1}^2}}/(n - 1)\\UCL = \overline {{s^2}} \chi _{.001,n - 1}^2/(n - 1)\end{aligned}\)
Construct the corresponding chart for the data of Exercise\(11\). (Hint: The lower- and upper-tailed chi-squared critical values for\(5\)df are\(.210\)and\(20.515\), respectively.)
For what\(\bar x\)values will the LCL in a\(c\)chart be negative?
Containers of a certain treatment for septic tanks are supposed to contain \(16oz\) of liquid. A sample of five containers is selected from the production line once each hour, and the sample average content is determined. Consider the following results: \(15.992,16.051,16.066,15.912\), \(16.030,16.060,15.982,15.899,16.038,16.074,16.029\), \(15.935,16.032,15.960,16.055\). Using \(\Delta = .10\) and\(h = .20\), employ the computational form of the CUSUM procedure to investigate the behaviour of this process.
If a process variable is normally distributed, in the long run virtually all observed values should be between饾潄-3饾潏and饾潄+3饾潏, giving a process spread of 6饾潏.
a.With LSL and USL denoting the lower and upper specification limits, one commonly used process capability indexis Cp = (USL 鈥 LSL)/6饾潏. The value Cp= 1 indicates a process that is only marginally capable of meeting specifications. Ideally, Cp should exceed 1.33 (a 鈥渧ery good鈥 process). Calculate the value of Cp for each of the cork production processes described in the previous exercise, and comment.
b. The Cp index described in (a) does not take into account process location. A capability measure that does involve the process mean is Cpk = min {(饾潏USL -饾潄)/3饾潏, (饾潄鈥 LSL)/3饾潏} Calculate the value of Cpk for each of the cork production processes described in the previous exercise, and comment. (Note: In practice, m and s have to be estimated from process data; we show how to do this in Section 16.2)
c. How do Cp and Cpk compare, and when are they equal?
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