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However, a point that plots outside of the control limits is interpreted as evidence that the process is out of control, and investigation and corrective action are required to nd and eliminate the assignable cause or causes responsible for this behavior The sample points on the control chart are usually connected with straight-line segments so that it is easier to visualize how the sequence of points has evolved over time Even if all the points plot inside the control limits, if they behave in a systematic or nonrandom manner, this is an indication that the process is out of control For example, if 18 of the last 20 points plotted above the center line but below the upper control limit and only two of these points plotted below the center line but above the lower control limit, we would be very suspicious that something was wrong.

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2, S has been permuted to Z, which then becomes L due to the stecker cable between L and Z Finally, the.

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If the process is in control, all the plotted points should have an essentially random pattern Methods designed to nd sequences or nonrandom patterns can be applied to control charts as an aid in detecting out-of-control conditions A particular nonrandom pattern usually appears on a control chart for a reason, and if that reason can be found and eliminated, process performance can be improved There is a close connection between control charts and hypothesis testing Essentially, the control chart is a test of the hypothesis that the process is in a state of statistical control A point plotting within the control limits is equivalent to failing to reject the hypothesis of statistical control, and a point plotting outside the control limits is equivalent to rejecting the hypothesis of statistical control..

We give a general model for a control chart. Let W be a sample statistic that measures some quality characteristic of interest, and suppose that the mean of W is W and the standard deviation of W is W.* Then the center line, the upper control limit, and the lower control limit become

(mod n ) )

(16-1)

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where k is the distance of the control limits from the center line, expressed in standard deviation units. A common choice is k 3. This general theory of control charts was rst proposed by Dr. Walter A. Shewhart, and control charts developed according to these principles are often called Shewhart control charts. The control chart is a device for describing exactly what is meant by statistical control; as such, it may be used in a variety of ways. In many applications, it is used for online process monitoring. That is, sample data are collected and used to construct the control chart, and if the sample values of x (say) fall within the control limits and do not exhibit any systematic pattern, we say the process is in control at the level indicated by the chart. Note that we may be interested here in determining both whether the past data came from a process that was in control and whether future samples from this process indicate statistical control. The most important use of a control chart is to improve the process. We have found that, generally 1. 2. Most processes do not operate in a state of statistical control. Consequently, the routine and attentive use of control charts will identify assignable causes. If these causes can be eliminated from the process, variability will be reduced and the process will be improved.

arid T is made public. Alice s private key consists of S and m-l (mod n ) . Suppose Bob wants to send a message of T bits to Alice. Bob first converts his plaintext into a binary block B . He then uses the 1 bits of B to select thc elerrient,~ T , which are then summed to give the ciphertext block C. Alice of recovers the plaintext B , by using the private key to compute Cm- (mod n ) , arid solves using her superincreasing knapsack. To encrypt longer messages, inult,iple blocks are encrypted. To make things more concrete, consider the following example. Suppose that Alice chooses the superincreasing knapsack

This process-improvement activity using the control chart is illustrated in Fig. 16-2. Notice that:

* Note that sigma refers to the standard deviation of the statistic plotted on the chart (i.e., deviation of the quality characteristic.

5.2 Evening Ceremony, Williamsburg, Virginia. We spent two days seeing the sights and living the history of our nation in Williamsburg. On the nal evening, I reshot a scene I had taped the night before in color, the Evening Ceremony, a second time in Sepia for a nostalgic look. Taken at ISO 400, f/8, with a 29.4mm lens setting.

The control chart will only detect assignable causes. Management, operator, and engineering action will usually be necessary to eliminate the assignable cause. An action plan for responding to control chart signals is vital.

S = (2.3.7,14, 30,57.120,251),

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