Statistical data collection

The intensity of the fire will determine which stages the fire will traverse on its way from ignition to decay. The National Fire Protection Association (NFPA) stores statistical data collected from the fire marshalls reports. It classifies [1] fires as ... 

B92015 Quality Assurance Project Plan for the National Pesticide Survey of Drinking Water Wells Survey Statistics, Data Collection, and Processing... 

A stepwise approach to DQA identifies different tasks that may be performed by individuals with different expertise. For example, a less experienced chemist may verify the data package content (Step 2), whereas a more experienced chemist may perform data evaluation (Step 3). For a statistical data collection design, a statistician may be involved in the assessment of data relevancy (Step 6). A database manager may be involved at several steps if the EDDs are part of laboratory deliverables and if completeness is calculated. 

The study focused on studying the effect of vehicle emissions by taking the SQU facihty as an area source. The domain area was considered as a polygon with an area of 5.9 km. Table 4 represents statistical data collection for average number of vehicles entering the SQU... 

Table 4. Statistical data collection for average number of cars entering the domain within selected timing.

The reliability characteristics and the objective function were investigated for a wide range of machine age on the basis of statistical data for the years 2000-2007. Only the most reliable statistical data (collected mainly by the company) were used in the analysis. 

Kolowrocki, K. Soszynska, J. 2008. Preliminary statistical data collection of the oil pipeline system technical components reliability. WP 6 - SubTask 6.2.1 - English -Poland-Singapore Joint Project. MSHE Decision No. 63/N-Singapore/2007/0. 

In 2002, Turkey adopted the NUTS-IBBS (The Nomenclature of territorial Units for Statistics), a system of regional statistical data collection that is used in the EU. This system provides a new regional mapping of three levels. NUTS I and NUTS II levels cover 12 regions and 26 regions respectively whereas NUTS m level repaesents all of the 81 cities in Turkey. 

Collection. Once the data factors have been defined, statisticians must collect them from the population being studied. Experimental design also plays a role in this step. Statistical data collection must be thorough and must follow the rules of the study. For example, if a survey is mailed to one thousand high school graduates and only three respond, more data must be collected before the survey s findings can be considered valid. Statisticians also must ensure that collected data are accurate by finding a way to check the reliability of answers. 

HSE changed the basis of its statistical data collection from calendar years to fiscal years in 1991. 

Statistical data collection is based on various classifications - the UN Systems of National Accounts (SNA), the Intemational Standard Classification for Education (ISCED), the Intemational Standard Classification for Occupations (ISCO) and the Intemational Standard Classification of all Industrial Activities (ISIC), and is collected with a focus on subject - for example, numbers of graduates and human resources, amount of financial resources, investment and expenditure, and the number of papers published, copyrights and patents issued. It is important to note that metrics and indicators relate to information that is requested or required at national and international level (in line with the need for internationally harmonious and comparable data). Statistics of science and technology relate to numbers that are readily available and measurable, and are not necessarily metrics and indicators that give the best picture of engineering, science or technology. How good the information and data is, and... 

In the 1980 s, the maritime classification societies, commercial institutions and other authorities realised the importance of statistical data collection on failure or repair data and eventually, data on general accident statistics were provided (HSE (1992a, b)). These data give general trends and are not directly useable in quantitative assessments. By far the most useful sets of statistics on marine accidents are presented in the publications of the UK Protection and Indemnity (P I) Club of insurers (P I Club (1992)). 

The raw data collected during the experiment are then analyzed. Frequently the data must be reduced or transformed to a more readily analyzable form. A statistical treatment of the data is used to evaluate the accuracy and precision of the analysis and to validate the procedure. These results are compared with the criteria established during the design of the experiment, and then the design is reconsidered, additional experimental trials are run, or a solution to the problem is proposed. When a solution is proposed, the results are subject to an external evaluation that may result in a new problem and the beginning of a new analytical cycle. 

The following experiments may he used to introduce the statistical analysis of data in the analytical chemistry laboratory. Each experiment is annotated with a brief description of the data collected and the type of statistical analysis used in evaluating the data. 

In this experiment students measure the length of a pestle using a wooden meter stick, a stainless-steel ruler, and a vernier caliper. The data collected in this experiment provide an opportunity to discuss significant figures and sources of error. Statistical analysis includes the Q-test, f-test, and F-test. 

Vitha, M. F. Carr, P. W. A Laboratory Exercise in Statistical Analysis of Data, /. Chem. Educ. 1997, 74, 998-1000. Students determine the average weight of vitamin E pills using several different methods (one at a time, in sets of ten pills, and in sets of 100 pills). The data collected by the class are pooled together, plotted as histograms, and compared with results predicted by a normal distribution. The histograms and standard deviations for the pooled data also show the effect of sample size on the standard error of the mean. 

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. 

Decision Process. In many cases, the decision regarding the need for exposure reduction measures is obvious and no formal statistical procedure is necessary. However, as exposure criteria are lowered, and control becomes more difficult, close calls become more common, and a logical decision-making process is needed. A typical process is shown in Eigure 2. Even when decision making is easy it is useful to remember the process and the assumptions involved. Based on an evaluation, decisions are made regarding control. The evaluation and decision steps caimot be separated because the conduct of the evaluation, the strategy, measurement method, and data collection are all a part of the decision process. 

Emphasis for prevention will be on changing individual behavior by symbolic or tangible rewards based on statistical evidence from the data collection system. "Hard" performance indicators such as lost time incidents will therefore be preferred to "softer" data such as near-miss reports. Accident prevention will also emphasize motivational campaigns designed to enhance the awareness of hazards and adherence to rules. If a severe accident occurs, it is likely that disciplinary sanctions will be applied. 

Three reports have been issued containing IPRDS failure data. Information on pumps, valves, and major components in NPP electrical distribution systems has been encoded and analyzed. All three reports provide introductions to the IPRDS, explain failure data collections, discuss the type of failure data in the data base, and summarize the findings. They all contain comprehensive breakdowns of failure rates by failure modes with the results compared with WASH-1400 and the corresponding LER summaries. Statistical tables and plant-specific data are found in the appendixes. Because the data base was developed from only four nuclear power stations, caution should be used for other than generic application. 

It should be noted that the data collection and conversion effort is not trivial, it is company and plant-specific and requires substantial effort and coordination between intracompany groups. No statistical treatment can make up for inaccurate or incomplete raw data. The keys to valid, high-quality data are thoroughness and quality of personnel training comprehensive procedures for data collection, reduction, handling and protection (from raw records to final failure rates) and the ability to audit and trace the origins of finished data. Finally, the system must be structured and the data must be coded so that they can be located within a well-designed failure rate taxonomy. When done properly, valuable and uniquely applicable failure rate data and equipment reliability information can be obtained. 

The calculation is advanced by a suitable timestep, typically a femtosecond, and statistical data is collected for comparison with experiment. 

Control laboratories in the canned food industry are usually divorced from the research organization to a lesser degree than is the case in the chemical and allied industries. For this reason, a closer relationship exists between the problems of the control laboratory and the research laboratory. Although from a research standpoint this condition is often considered undesirable, it has considerable merit in the case of the canned food industry, in which production may be seasonal and often of rather short duration. The collection of control data in many instances may also serve for research purposes—for example, in the case of soil analyses, which may be correlated with agricultural research designed to improve crop yields. Because the variables which affect the quality of canned foods must usually be investigated rather extensively, and often over a period of more than one year, the application of statistical methods to data collected for control purposes can conceivably make a substantial contribution to a research program. 

When analyzing the data from Table 6.2, one must keep in mind that as of the beginning of the 1970s, data collection on chemical poisoning statistics was transferred from Minzdrav s general Health and Epidemiological Service to an independent Third Main Directorate in the same Ministry - a secret division linked to defense work in the nuclear and chemical industries. 

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