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A sample of 200 ROM computer chips was selected on each of 30 consecutive days, and the number of nonconforming chips on each day was as follows: \(10,18,24,17,37,19,7,25,11,24,29,15,16,21,18,17,15,22,12\$ ,\$ 20,17,18,12,24,30,16,11,20,14,28\)Construct a\(p\)chart and examine it for any out-of-control points.

Short Answer

Expert verified

The process is out-of-control.

Step by step solution

01

a)Step 1: Determine UCL and LCL

The\(p\)chart for the fraction of defective items has lower and upper control limits given by

\(\begin{aligned}{*{20}{r}}{LCL = \bar p - 3\sqrt {\frac{{\bar p(1 - \bar p)}}{n}} }\\{UCL = \bar p + 3\sqrt {\frac{{\bar p(1 - \bar p)}}{n}} ,}\end{aligned}\)

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

The center line, from the fact that

\(\sum\limits_{i = 1}^k {{{\hat p}_i}} = \frac{{{x_1} + {x_2} + \ldots + {x_k}}}{n} = \frac{{567}}{{200}} = 2.835,\)

is given by

\(\bar p = \frac{{2.835}}{{30}} = 0.0945.\)

0The\(LCL\)is

\(\begin{aligned}{l}LCL = \bar p - 3\sqrt {\frac{{\bar p(1 - \bar p)}}{n}} = 0.0945 - 3 \cdot \sqrt {\frac{{0.0945 \cdot (1 - 0.0945)}}{{200}}} \\ = 0.0325.\end{aligned}\)

The\(UCL\)is

\(\begin{aligned}{l}UCL = \bar p + 3\sqrt {\frac{{\bar p(1 - \bar p)}}{n}} = 0.0945 + 3 \cdot \sqrt {\frac{{0.0945 \cdot (1 - 0.0945)}}{{200}}} \\ = 0.1565\end{aligned}\)

There is one out-of-control point, on the fifth day, because

\(\frac{{37}}{{200}} > 0.1565 = UCL\)

02

Mapping the chart

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Most popular questions from this chapter

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?

The number of scratches on the surface of each of 24 rectangular metal plates is determined, yielding the following data: \(8,1,7,5,2,0,2,3,4,3,1,2,5,7,3,4\), \(6,5,2,4,0,10,2,6\). Construct an appropriate control chart, and comment.

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.

Consider the double-sampling plan for which both sample sizes are 50 . The lot is accepted after the first sample if the number of defectives is at most 1 , rejected if the number of defectives is at least 4 , and rejected after the second sample if the total number of defectives is 6 or more. Calculate the probability of accepting the lot when \(p = .01,.05\), and \(.10\).

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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