Control charts Outliers

FIGURE 11.22 Control charts and outliers, (a) pEC50 values (ordinates) run as a quality control for a drug screen over the days on which the screen is run (abscissae). Dotted lines are the 95% c.l. and the solid lines the 99.7% c.l. Data points that drift beyond the action lines indicate significant concern over the quality of the data obtained from the screen on those days, (b) The effect of significant outliers on the criteria for rejection. For the data set shown, the inclusion of points A and B lead to a c.l. for 95% confidence that includes point B. Removal of point A causes the 95% limits to fall below points B, causing them to be suspect as well. Thus, the presence of the data to be possibly rejected affects the criteria for rejection of other data. 

All uncertainty estimates start with that associated with the repeatability of a measured value obtained on the unknown. It is neither required for the sake of quality control, nor could it always be economically justified, to make redundant determinations of each measured value, such as would be needed for complete statistical control. Repeat measurements of a similar kind under the laboratory s typical working conditions may have given satisfactory experience regarding the range of values obtained under normal operational variations of measurement conditions such as time intervals, stability of measurement equipment, laboratory temperature and humidity, small disparities associated with different operators, etc. Repeatability of routine measurements of the same or similar types is established by the use of RMs on which repeat measurements are made periodically and monitored by use of control charts, in order to establish the laboratory s ability to repeat measurements (see sect, entitled The responsible laboratory above). For this purpose, it is particularly important not to reject any outlier, unless cause for its deviation has been unequivocally established as an abnormal blunder. Rejection of other outliers leads a laboratory to assess its capabilities too optimistically. The repeatability in the field of a certified RM value represents the low limit of uncertainty for any similar value measured there. 

The first data evaluation should regard the quality of data. Well-known methods are quality control charts (WHO, 1981). Another method is using outlier statistics. The first... 

Point Outliers. Statistical Process Control. The variability of a property is just the deviation of its value compared to the defined objectives. Although a process is controlled, variability will always be present. The causes of variability can be divided into two categories common causes and special causes. Statistical Process Control (SPC) is a technique that is provided to monitor, analyse, predict, control and improves the variability of a determined quality characteristic through the use of control charts and allows identifying special variability. 

Figure 1 shows the Xbar-S control chart for the Total Harmonic Distortion of Voltage (THDV) of the LI phase of Sample B, with rational subgroups grouped together by days and by weeks. In the figure it can be seen that in both cases the process is stable and is under statistical control, since there are no outliers, or any violations of additional rules for the detection of small standard shift variations, or unusual patterns (in the weekly control chart the control limits are variables because the sizes of the sample are different). In this situation, it can be concluded that the analyzed process is solely subject to natural variability and is therefore under statistical control. Consequently, it can... 

When control charts are developed for abnormal data distributions, as in the studied case, the results are as saw in Figure 3. In this figure, the control graph for the THDV of the LI phase of Sample A shows a very high number of outliers, 37 out of a set of 39 elements, which is totally contrary to the definition of outlier in section 2 of this work. This behavior persists even when submitting the data set to a Box-Cox transformation in an attempt to correct the distribution bias, the differences of the variances on the time axis or the possible nonlinearity of the data. 

Data selected over the 10-week period was used to construct the p-chart that appears in Figure 4.4. Examination of the chart indicates that the data used in the construction of the chart is considered to be in control since the plotted points consistently fall between the upper and lower control limits and the data points do not meet any of the criteria for an out-of-control chart. If and when the data used to construct the chart is out of control, the outliers need to be identified and the reasons for the problem data need to be addressed. A new control chart needs to be constructed with data that is considered in control. With the control chart constructed, the safety manager now collects data in the future and continues to plot the new sample values as they are obtained in the future. Evaluations and interpretations are made as new data points are added to the chart in terms of out-of-control data or the need to recalculate the control chart to narrow the control limits. 

Figure 21.5 indicates that sample 5 lies beyond the UCL for both the x and s control charts, while sample 15 is very close to a control limit on each chart. Thus, the question arises whether these two samples are outliers that should be omitted from the analysis. Table 21.2 indicates that sample 5 includes a very large value... 

When the excitations are random, the peak indicators behave like random variables. They will therefore follow a statistical distribution which can be inferred from several undamaged samples. Many tools have been developed to detect a change in that statistical distribution such as outlier analysis or hypothesis testing. In this contribution, control charts (Montgomery 2009 Ryan 2000) are presented. This tool of statistical quality control plots the features or quantities representative of their statistical distribution as a function of the samples. Different univariate or multivariate control charts exist but all these control charts are based on the same principle which is summarized in Fig. 5. 

---