Plot qualities

These story qualities can be broken down into two groups character qualities, and plot qualities. The primary character quality of a story is that we have to identify with the main character if we are to be engaged with the story. We have to become concerned with his or her dilemma, and we have to care about the outcome. 

Statistical process control charts (SPC charts) are used to plot quality parameter points from samples taken at different times during a run. Even if all of the points are within specifications, when they are plotted on a graph you may see quite clearly that there is a trend that in time will result in off-specification material unless an adjustment is made. An upset or out-of-control situation is both vividly revealed and documented by such a chart (see Figure 16-3). 

In the simplest case, for a pressure drawdown survey, the radial inflow equation indicates that the bottom hole flowing pressure is proportional to the logarithm of time. From the straight line plot ot pressure against the log (time), the reservoir permeability can be determined, and subsequently the total skin of the well. For a build-up survey, a similar plot (the so-called Horner plot) may be used to determine the same parameters, whose values act as an independent quality check on those derived from the drawdown survey. 

As oversimplified cases of the criterion to be used for the clustering of datasets, we may consider some high-quality Kohonen maps, or PCA plots, or hierarchical clustering. 

The chart consists of a central line and two pairs of limit lines or simply of a central line and one pair of control limits. By plotting a sequence of points in order, a continuous record of the quality characteristic is made available. Trends in data or sudden lack of precision can be made evident so that the causes may be sought. 

The principal tool for performance-based quality assessment is the control chart. In a control chart the results from the analysis of quality assessment samples are plotted in the order in which they are collected, providing a continuous record of the statistical state of the analytical system. Quality assessment data collected over time can be summarized by a mean value and a standard deviation. The fundamental assumption behind the use of a control chart is that quality assessment data will show only random variations around the mean value when the analytical system is in statistical control. When an analytical system moves out of statistical control, the quality assessment data is influenced by additional sources of error, increasing the standard deviation or changing the mean value. 

Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products.Two types of control charts are commonly used in quality assurance a property control chart in which results for single measurements, or the means for several replicate measurements, are plotted sequentially and a precision control chart in which ranges or standard deviations are plotted sequentially. In either case, the control chart consists of a line representing the mean value for the measured property or the precision, and two or more boundary lines whose positions are determined by the precision of the measurement process. The position of the data points about the boundary lines determines whether the system is in statistical control. 

Another important quality assessment tool, which provides an ongoing evaluation of an analysis, is a control chart. A control chart plots a property, such as a spike recovery, as a function of time. Results exceeding warning and control limits, or unusual patterns of data indicate that an analysis is no longer under statistical control. 

Known samples should also be run to verify the accuracy and precision of the routine methods to be used during the unit test. Poor quality will manifest itself as poor precision, measurements inconsistent with plant experience or laboratory history, and disagreement among methods. Plotting of laboratory analysis trends wiU help to determine whether calibrations are drifting with time or changing significantly. Repeated laboratory analyses will establish the confidence that can be placed in the results. 

From the quality-eost arguments made in Seetion 1.2, it is possible to plot points on the graph of Oeeurrenee versus Severity and eonstruet lines of equal failure eost (% isoeosts). Figure 2.22 shows this graph, ealled a Conformability Map. Beeause of uneertainty in the estimates, only a broad band has been defined. 

If air quality data at a receptor for any one averaging time are lognormally distributed, these data will plot as a straight line on log probability graph paper (Fig. 4-9) which bears a note Sg = 2.35. Sg is the standard geometric deviation about the geometric mean (the geometric mean is the Nth root of the product of the n values of the individual measurements). 

Larsen (18-21) has developed averaging time models for use in analysis and interpretation of air quality data. For urban areas where concentrations for a given averaging time tend to be lognormally distributed, that is, where a plot of the log of concentration versus the cumulative frequency of occurrence on a normal frequency distribution scale is nearly linear,... 

To determine trends in customer satisfaction and dissatisfaction you will need to make regular surveys and plot the results, preferably by particular attributes or variables. The factors will need to include quality characteristics of the product or service as well as delivery performance and price. The surveys could be linked to your improvement programs so that following a change, and allowing sufficient time for the effect to be observed by the customer, customer feedback data could be secured to indicate the effect of the improvement. 

Plot properties of the fresh and equilibrium catalysts ensure that the catalyst vendor is meeting the agreed quality control specifications. Verify that the catalyst vendor has the latest data on feed properties, unit condition, and target products. Verify the fresh makeup rate. Check for recent temperature excursions in the regenerator or afterburning problems. 

In burn-out experiments, a test section is part of a loop which may be open or closed, and the question arises as to whether or not any of the loop equipment, such as condensers, heaters, pumps, or pipe fittings, have any significant effect on the burn-out flux. This issue came to prominence at the Boulder Heat Transfer Conference in 1961 with a Russian paper by Aladyev (A4) describing burn-out experiments in which a branch pipe, connecting to a small vessel, was fitted close to the test section inlet. The test section itself was a uniformly heated tube 8 mm in diameter and 16 cm long. The results are reproduced in Fig. 9, and show burn-out flux plotted against exit steam quality. Curve (A) was obtained with the branch vessel filled with cold water,... 

It can be seen that k in Eq. (10) replaces the system-describing parameters L and Ah in Eq. (1). A direct test of the hypothesis is therefore to plot (j> against k for fixed values of P, G, and d, with L and Ah varying. For the hypothesis to be correct, the data points must all lie on a smooth curve. Experience shows, however, that plotting (f> against k often produces an undue amount of scatter which may obscure and distort any true relationship existing. This enhanced scatter is caused by the cumulative effect of experimental errors in the various terms in the heat-balance equation from which the quality k is derived. 

Again, Eqs. (28) and (48) form a system of two equations and two unknowns, which can be solved and yield the values of the exponents q, and q2. Their difference A=r 1— q2, which expresses the quality of adhesion, is called the adhesion coefficient. The values of the exponents q, and q2> as well as of their difference A were given in Table 2 and plotted in Fig. 15 for the glass-fiber composites given in Ref. 22). 

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