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If you do not have effective process control, you are paying someone to make a nonconforming product 3 Control charts prevent unnecessary process adjustments A control chart can distinguish between background noise and abnormal variation; no other device, including a human operator, is as effective in making this distinction If process operators adjust the process based on periodic tests unrelated to a control chart program, they will often overreact to the background noise and make unneeded adjustments These unnecessary adjustments can result in a deterioration of process performance In other words, the control chart is consistent with the if it isn t broken, don t x it philosophy 4 Control charts provide diagnostic information Frequently, the pattern of points on the control chart will contain information that is of diagnostic value to an 1..

to illustrate the architecture independence and composability of ATaG programs, which allow the process of developing ATaG program libraries to be entirely decoupled from the process of selecting and compiling a set of programs from the library onto the desired target deployment.

Now that s closer to the truth! Remember that the replace() method returns the modi ed String, rather than modifying it in place, so you assign the result back to quote to immediately apply the replacements. Now let s try replacing a pattern with a string.

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experienced operator or engineer. This information allows the operator to implement a change in the process that will improve its performance. Control charts provide information about process capability. The control chart provides information about the value of important process parameters and their stability over time. This allows an estimate of process capability to be made. This information is of tremendous use to product and process designers.

Control charts are among the most effective management control tools, and they are as important as cost controls and material controls. Modern computer technology has made it easy to implement control charts in any type of process, because data collection and analysis can be performed on a microcomputer or a local area network terminal in realtime, online at the work center.

var document:String = "My name is John Doe, SSN 123-45-6789," + "I live at 120 Birch St..."; var ssn:RegExp = /\d{3}-\d{2}-\d{4}/g; document = document.replace(ssn, "<SSN REMOVED>"); trace(document); //My name is John Doe, SSN <SSN REMOVED>, I live at 120 Birch St...

16-4.2

The example here searches for all patterns that look like a social security number (SSN) and sanitizes the document so that it contains no SSNs but indicates that they have been removed. If you have capture groups inside your expression, when replacing text you can use the captured text as a variable inside the replacement string. For example, let s change all instances of my name is X to X is my name.

To illustrate these ideas, we give a simpli ed example of a control chart. In manufacturing automobile engine piston rings, the inside diameter of the rings is a critical quality characteristic. The process mean inside ring diameter is 74 millimeters, and it is known that the standard deviation of ring diameter is 0.01 millimeters. A control chart for average ring diameter is shown in Fig. 16-3. Every hour a random sample of ve rings is taken, the average ring diameter of the sample (say x) is computed, and x is plotted on the chart. Because this control chart utilizes the sample mean X to monitor the process mean, it is usually called an X control chart. Note that all the points fall within the control limits, so the chart indicates that the process is in statistical control. Consider how the control limits were determined. The process average is 74 millimeters, and the process standard deviation is 0.01 millimeters. Now if samples of size n 5 are taken, the standard deviation of the sample average X is 1n 0.01 15 0.0045

var introduction:String = "My name is Roger; but I ll have you know MY name is Henry!"; trace(introduction.replace(/my name is (\w+)/gi, "$1 is my name")); //Roger is my name; but I ll have you know Henry is my name!

Therefore, if the process is in control with a mean diameter of 74 millimeters, by using the central limit theorem to assume that X is approximately normally distributed, we would expect approximately 100(1 )% of the sample mean diameters X to fall between 74 z 2(0.0045) and 74 z 2(0.0045). As discussed above, we customarily choose the

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