Statistical indicators trends

Direct comparisons of drug-related death statistics between countries are, however, misleading because of the lack of harmonised definitions and methodologies. The EMCDDA is collaborating with Eurostat, the World Health Organisation and EU Member States to improve this situation (see box above). If definitions and methods remain consistent within countries, however, the statistics can indicate trends overtime. 

It is difficult to identify effective lagging indicators for use with process safety. The most obvious problem is that major PSIs do not occur frequently enough to develop a statistically significant trend such as that shown in Figure 2.3. If many facilities and companies pool their data it may be possible to that some trending results can be developed. However, such results are always open to doubt, not least because different organizations define terms differently. For example, the Baker report (Baker, 2007) provides a list of events that fall under the term fire. That list includes a fault in a motor control center. It is questionable as to how many organizations would call such an event a fire unless it resulted in actual flames. 

Support for the anticarcinogenic effects of selenium is widespread [34,35]. However, a study of Se in diet and its relation to breast cancer found no statistically significant trend for any of the indicators of selenium [36]. 

Patent statistics are an important instrument for your own search supervision and the analysis of competitors. They are used in the area of market research as an alert system indicating trends and new developments. Analysis can be executed along different criteria, i.e. ... 

In the maximum-likelihood method used here, the "true" value of each measured variable is also found in the course of parameter estimation. The differences between these "true" values and the corresponding experimentally measured values are the residuals (also called deviations). When there are many data points, the residuals can be analyzed by standard statistical methods (Draper and Smith, 1966). If, however, there are only a few data points, examination of the residuals for trends, when plotted versus other system variables, may provide valuable information. Often these plots can indicate at a glance excessive experimental error, systematic error, or "lack of fit." Data points which are obviously bad can also be readily detected. If the model is suitable and if there are no systematic errors, such a plot shows the residuals randomly distributed with zero means. This behavior is shown in Figure 3 for the ethyl-acetate-n-propanol data of Murti and Van Winkle (1958), fitted with the van Laar equation. 

Figure 4 illustrates the trend in adiabatic flame temperatures with heat of combustion as described. Also indicated is the consequence of another statistical result, ie, flames extinguish at a roughly common low limit (1200°C). This corresponds to heat-release density of ca 1.9 MJ/m (50 Btu/ft ) of fuel—air mixtures, or half that for the stoichiometric ratio. It also corresponds to flame temperature, as indicated, of ca 1220°C. Because these are statistical quantities, the same numerical values of flame temperature, low limit excess air, and so forth, can be expected to apply to coal—air mixtures and to fuels derived from coal (see Fuels, synthetic). 

Maximum rate of change alert The second alert (i.e., maximum rate of change alert) is used to automatically notify the operator that based on statistical data the rate of degradation has increased above the pre-selected norm. Since the vibration amplitudes of all machine-trains increase as normal wear occurs, the statistical rate of this normal increase should be trended. A drastic change in this rate is a major indication that a problem is developing. 

Historical data on the indicator. Existing information on the statistical variation, bias, and other interpretational attributes of potential biological indicators should be examined and considered in the design of a sampling program for assessing trends in mercury bioaccumulation. 

Of46,135 reflections measured (29,973 with I > 2a(T)), only 156 reflections were missing to sin 9/A= 1.34 A-1 5102 reflections were unique of which 2681 had been measured more than nine times (symmetry equivalents plus multiple measurements). The merging R values were R1 = 0.037 and R2 = 0.024 for 4809 accepted means. Examination of the reflection statistics (Table 2) with respect to F2/charge density study. 

Table 35-3 illustrates the ANOVA results comparing laboratories (i.e., different locations) performing the same METHOD A for analysis. This statistical test indicates that for the mid-level concentration spiked samples (i.e. 4 and 4 at 3.40 and 3.61% levels, respectively) difference in reported average values occurred. However, this trend did not continue for the highest concentration sample (i.e., Sample No. 6) with a concentration of 3.80%. The Lab 1 was slightly lower in reported value for Samples 4 and 5. There is no significant systematic error observed between laboratories using the METHOD A. 

Table 35-4 reports ANOVA comparing the METHOD B procedure to the METHOD A procedure for combined laboratories. Thus the combined METHOD B analyses for each sample were compared to the combined METHOD A analyses for the same sample. This statistical test indicates whether there is a significant bias in the reported results for each method, irrespective of operator or location. An apparent trend is indicated using this statistical analysis, that trend being a positive bias for METHOD B as compared to... 

The Student s (W.S. Gossett) /-lest is useful for comparisons of the means and standard deviations of different analytical test methods. Descriptions of the theory and use of this statistic are readily available in standard statistical texts including those in the references [1-6]. Use of this test will indicate whether the differences between a set of measurement and the true (known) value for those measurements is statistically meaningful. For Table 36-1 a comparison of METHOD B test results for each of the locations is compared to the known spiked analyte value for each sample. This statistical test indicates that METHOD B results are lower than the known analyte values for Sample No. 5 (Lab 1 and Lab 2), and Sample No. 6 (Lab 1). METHOD B reported value is higher for Sample No. 6 (Lab 2). Average results for this test indicate that METHOD B may result in analytical values trending lower than actual values. 

There is doubtful statistical significance in the trend observed in the bondlengths. However, it appears that the similarity in the data for the samples is indicative of similar local structure at all stages of polymer deposition. These values correlate very well with the known coordination number of six and a Ru-N distance of 2.056 A. Q4)... 

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