Statistics control chart

The objective of the microbial monitoring program is to obtain representative estimates of the bioburden of the environment. When data are compiled and analyzed, any trends should be evaluated by trained personnel. While it is important to review environmental results on the basis of recommended and specified frequency, it is also critical to review results over extended periods to determine whether or not trends are present. Trends can be visualized through the construction of statistical control charts that include alert and action levels. The microbial control of controlled environments can be assessed in part on the basis of these trend data. Periodic reports or summaries should be issued to alert the responsible manager [13]. 

The concept of a confidence interval may be used to set up a statistical control chart on the mean. Let us consider the reactor from Example 1.14. Suppose we want to use the results of the 11 runs to establish a procedure for operation of the reactor in future runs. 

Table 5.4 summarizes common acceptance criteria for laboratory QC checks. Laboratories rarely exceed these method-prescribed criteria for trace element and inorganic analyses. For organic analyses, laboratories generate their own acceptance criteria by means of statistical control charts as described in Chapter 4.6.1.3. 

French Standard, 1995. NFX 06-031-1, Application of statistics, Control Charts - Part 1 Shewhart control charts by variables. 

The criteria for the choice of the CRM are not different from the criteria to select the material for the preparation of a laboratory reference material for method development, statistical control charts etc. The difference lies in the availability of adequate CRMs from reliable suppliers and the level of compromise which the analyst must make between an ideal situation and the reality of what is on offer. Massart and co-workers have proposed a principle component analysis to help select the best adapted CRMs available on the market to verify AAS analysis of foodstuffs [10], Their approach took into account the analytes as well as the matrix composition. Besides the fact that they highlighted a lack of sorts of CRM, in particular those having a fatty matrix, they demonstrated that such a statistical approach can help in the most appropriate selection of materials. Boenke also proposed a systematic approach for the choice of materials to be certified for mycotoxins [11] and which could be followed by potential users. The selection of the CRM by the analyst should include a certain number of parameters this can cover the following properties to fulfil the intended purpose level of concentration of the analytes ... 

SPC helps in detecting, identifying, and eliminating unpredictable sources of variability in the process. Moreover, it helps monitoring the process by issuing signals whenever deviations from in-control conditions are detected. Statistical control charts are the basic tools to implement SPC in manufacturing processes. 

The main goal of statistical control charts is to identify the presence or the occurrence of special causes of variability in order to remove them and keep the process in control, according to the continuous improvement paradigm. 

A statistical control chart is a statistical procedure that identifies out-of-control conditions as effected by special variability causes through the systematic analysis of the output of a process (Montgomery 2013 Alwan 2000). 

Control Charts for Small Shifts Since traditional control charts based on variables perform well only for consistent shifts of the observed sample statistics, control charts for detecting small shifts have been developed. Different from traditional control charts, they do... 

The multivariate 7 and Q statistics control charts for the fluorescence spectra of the one of the 96-microreactor arrays discussed in Section 5.1 are presented in Figure 5.13.These control charts illustrate that several samples exceed the 95% confidence limits for the T and Q statistics described by... 

FIGURE 27.7 When using an NIR method for process control, statistical control charting can be used to set upper and lower control limits. This method clearly shows the difference between one sample with an unusual result vs. a series of samples that are trending toward the upper or lower control limit. 

HGURE 2.6 Statistical control chart with incident rate variation... 

Statistical control charts help the site to identify what is meaningful versus what is not statistically valid or random. These charts enable the user to focus attention on real, significant variations as opposed to imagined problems or special cases. 

Take the square root of this average and multiply this by two. Add this number to the average rate and this munber becomes the upper control hmit. Subtract it from the average and you have the lower control hmit. Statistical control charts have three lines on them, an average or base rate, an upper control hmit, and a lower control limit. 

This is a very basic method for calculating upper and lower control limits. Other methods can also be used to calculate these hmits. To ensure that the statistical control charts are as reliable as practical, keep several guidelines in mind. First, use accident rates that have as many sets of data as possible. For example, an aU injury/iUness frequency rate works better than a lost time frequency rate. Second, try to use at least twenty sets of data in calculating the base rate or average. In our example we only used five data points for practical purposes. However, they represented 60 individual monthly frequency rates. 

Interpreting statistical control charts properly helps to reduce the over-reaction to accident rates that are not statistically valid. Figure 4.2 lists several guideUnes to help you interpret the data provided by statistical control charts. 

FIGURE 4.2 Rules for Interpreting statistical control charts... 

Miner s safety performance recognition, circa 1920s 20 The systems model of health and safety management 24 The size of mobile mine equipment continues to increase 27 Simple model of outcomes dependent on culture and systems 32 Statistical control chart with incident rate variation 33 Universal Copper and Metals Mine accident frequency rate control chart 55... 

To implement Ml image process control, an IR line scanner is placed at the oven exit, scaiming across each blank as it leaves the oven. The forward movement of the part, combined with the fast response of the line scanner, provides the Ml thermal map to the computer. A Ml statistical control chart is software-generated, based on mean temperature and standard deviation over the entire surface. The oven heater is zoned and the computer interfaced to a relay control board with one relay managing a set-point controller for each zone. The statistics for each control zone are calculated based on the corresponding section on the part, and a closed-loop statistical process control algorithm is implemented. The result is seen on the 3D strip map presentation shown in Fig. 10.8(b). The color hues indicate temperatures in accordance with the scale at the left. The control level temperature is about 300°F. 

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