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The accompanying observations are numbers of defects in\(25\)\(1\)-square-yard specimens of woven fabric of a certain type: \(3,7,5,3,4,2,8,4,3,3,6,7,2,3,2,4,7,3,2,4,4\),\(1,5,4,6\). Construct a\(c\)chart for the number of defects.

Short Answer

Expert verified

The process is incontrol.

Step by step solution

01

Find LCL and UCL

The \(c\) chart for the number of defectives in a unit has lower and upper control limits given by

\(\begin{aligned}{l}LCL = \bar x - 3\sqrt {\bar x} \\UCL = \bar x + 3\sqrt {\bar x} ,\end{aligned}\)

and the center line is\(\bar x\). When \(LCL \le 0\) it is set to zero.

The center line, from the fact that

\(\sum\limits_{i = 1}^k {{{\hat x}_i}} = \frac{{{x_1} + {x_2} + \ldots + {x_k}}}{n} = \frac{{587}}{{100}} = 102\)

is given by

\(\bar x = \frac{{102}}{{25}} = 4.08\)

The \(LCL\) is

\(\begin{aligned}{l}LCL = \bar x - 3\sqrt {\bar x} = 4.08 - 3 \cdot \sqrt {4.08} \\ = - 2\end{aligned}\)

so set it to zero\(LCL = 0\).

The\(UCL\) is

\(UCL = \bar x + 3\sqrt {\bar x} = 4.08 + 3 \cdot \sqrt {4.08} \)

\( = 10.1\)

From the \(c\) chart one can notice that the process is in-control - no data exceeds \(LCL\) and \(UCL\).

02

Mapping the chart

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Most popular questions from this chapter

Consider the control chart based on control limits\({\mu _0} \pm 2.81\sigma /\sqrt n \)

a. What is the ARL when the process is in control?

b. What is the ARL when\(n = 4\)and the process mean has shifted to\(\mu = {\mu _0} + \sigma \)?

c. How do the values of parts (a) and (b) compare to the corresponding values for a\(3 - \sigma \)chart?

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?

Refer to Exercise 1 and suppose the ten most recent values of the quality statistic are .0493, .0485, .0490, .0503, .0492, .0486, .0495, .0494, .0493, and .0488. Construct the relevant portion of the corresponding control chart, and comment on its appearance.

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.

Consider the single-sample plan with \(c = 2\) and \(n = 50\), as discussed in Example 16.11, but now suppose that the lot size is \(N = 500\). Calculate \(P(A)\), the probability of accepting the lot, for \(p = .01,.02, \ldots ,.10\), using the hyper geometric distribution. Does the binomial approximation give satisfactory results in this case?

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