Quality data statistics

PTMT polytetramethylene-terephthalate QDS quality data statistics... 

In general, air quality data are classified as a function of time, location, and magnitude. Several statistical parameters may be used to characterize a group of air pollution concentrations, including the arithmetic mean, the median, and the geometric mean. These parameters may be determined over averaging times of up to 1 year. In addition to these three parameters, a measure of the variability of a data set, such as the standard deviation... 

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. 

Recke G, Wilier H, Lampkin N and Vaughan A (2004) Development of a European information system for organic markets - improving the scope and quality of statistical data , Proceedings of the 1st EISfOM (European Information System for Organic Markets) Seminar, Berlin, Germany, 26-27 April 2004. 

Cost. There is always a trade-off of cost versus data quality. Data quality can be improved by further method development, more extensive calibration, replicate analysis or better statistics. 

Wolff, G. T., J.-S. Siak, T. L. Chan, and P. E. Korsog, Multivariate Statistical Analyses of Air Quality Data and Bacterial Mutagenicity Data from Ambient Aerosols, Atmos. Environ., 20, 2231-2241 (1986). 

Probably the weight loss coupon approach is one of the must reliable methods and is widely used. The accuracy of the data is highly dependent on good techniques and on the statistical significance of the tests. The engineering quality data produced require the efforts of many people over a period of several weeks or months, which makes this information quite costly. However, it is the technique of first choice of many processors. 

Zetterqvist, L. Statistical Methods for Analysing Time Series of Water Quality Data, Doctoral Dissertation, Lund University, Department of Mathematical Statistics, 1989... 

Presenting the design, methodology, and data to be collected in a study protocol. This study protocol specifies the manner of data collection and addresses all methodological considerations necessary to ensure the collection of optimum quality data for subsequent statistical analysis. 

It is noted several times in this book that the goal of experimental methodology is to provide optimum quality data for subsequent statistical analysis. This is true, but there is also a very important intermediary between data acquisition and data analysis this is the field of clinical data management. In many cases, Data Management and Statistics fall under the same division within a company, and in some cases these tasks are handled by different divisions. Whichever is the case, it is vital to have statisticians involved in all discussions regarding database development and use. 

The previous chapter discussed the (currently) relatively loosely defined statistical approaches to safety data collected in clinical trials. In contrast, there are widely accepted statistical methods for demonstrating efficacy in clinical trials. As has been noted several times in this book, if the study design and methodology have been appropriate and have led to the collection of optimum quality data, the statistical analysis and interpretation of efficacy data are relatively straightforward. The clinical (biological) interpretation of efficacy data is typically not quite as clear-cut, but there are widely accepted methodologies that are very useful in this realm too. Of particular importance here is the expert judgment of the clinicians who will review the statistical results with the statisticians and the rest of the study team. 

In the multivariate distribution, it is assumed that measurements of objects or aliquots of material (e.g., a single trial) produces vectors, x having a multivariate normal distribution. The measurements of the p variables in a single object, such as xT = x,xi>1,. .., x J will usually be correlated. In fact, this is expected to be the case. The measurements from different objects, however, are assumed to be independent. The independence of measurements from object to object or from trial to trial may not hold when an instrument drifts over time, as with sets of p wavelengths in a spectrum. Violation of the tentative assumption of independence can have a serious impact on the quality of statistical inferences. As a consequence of these assumptions, we can make the following statements about data sets that meet the above criteria ... 

One further point Everything discussed up until now has been statistical quality control (SQC), not SPC, quality data and not process data. Process controls should be used to control quality, not the quality to control the process. This point seems to be missed in the majority of literature. Again, old habits die hard. 

Figure 3.11. First-order concentrations plots of data with same quality of statistical correlation left with random scatter, right with systematic bias.

Crawford, C.G., Slack, J.R. and Hirsch, R.M. (1983) Nonparametric Tests for Trends in Water Quality Data using the Statistical Analysis System. United States Geological Survey Report, 83-550. 

This functional definition makes clear that the discipline of Statistics is Indeed multi-faceted and essential throughout clinical trials. It is critical at the start of the clinical trial process so that a study can be designed appropriately to facilitate the collection of optimum quality data, which then need to be organized and managed correctly. These data are then described and analyzed, and the numerical results of these analyses are interpreted in the context of the particular study. Finally, the numerical results of the analyses and the authors interpretation of these results are presented to regulatory agencies to request permission to market the drug, and published in clinical communications to provide Information to physicians. 

This functional definition of Statistics may well contain some concepts and terms with which you are not familiar at this point, and that is fine. This book s goal is to make you familiar with these terms and concepts so that you will understand the statistical processes and procedures that are used in clinical trials. Individual chapters address different parts of this definition. However, it is important for us to emphasize here that the individual aspects presented in the chapters are really seamless components of one overall experimental approach to gaining knowledge, the discipline of Statistics. These components act together to ensure that high-quality data acquisition, correct analysis, and appropriate interpretations provide optimal answers to good research questions. 

Chapters 3 and 4 discuss how research questions are asked and answered in statistical language during clinical trials, and introduce the study designs and experimental methodologies that are used to acquire optimum quality data with which to answer our research questions. Chapter 5 discusses statistical ways of describing and summarizing these data. Chapter 6... 

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