Statistical evaluation, factors influencing

Statistical evaluation of the critical factors influencing intake calculations. 

Wu JS, Ho HO, Sheu MT. A statistical design to evaluate the influence of manufacturing factors on the material properties and functionalities of microcrystalline cellulose. Eur ] Pharm Sci 2001 12 417 25. 

In the interpretation of the numerical results that can be extracted from Mdssbauer spectroscopic data, it is necessary to recognize three sources of errors that can affect the accuracy of the data. These three contributions to the experimental error, which may not always be distinguishable from each other, can be identified as (a) statistical, (b) systematic, and (c) model-dependent errors. The statistical error, which arises from the fact that a finite number of observations are made in order to evaluate a given parameter, is the most readily estimated from the conditions of the experiment, provided that a Gaussian error distribution is assumed. Systematic errors are those that arise from factors influencing the absolute value of an experimental parameter but not necessarily the internal consistency of the data. Hence, such errors are the most difficult to diagnose and their evaluation commonly involves measurements by entirely independent experimental procedures. Finally, the model errors arise from the application of a theoretical model that may have only limited applicability in the interpretation of the experimental data. The errors introduced in this manner can often be estimated by a careful analysis of the fundamental assumptions incorporated in the theoretical treatment. 

It s obvious that there is a difference in this data. Why don t the statistics prove it This is a comment which has been heard by all consulting statisticians. There are numerous factors influencing a statistical evaluation. Among these are ... 

In order to make a meaningfiil statistical evaluation of the results of accountancy verifications, the inspector s measurements must be planned in a way, which will provide independent estimates of the overall measurement uncertainties. All factors influencing these uncertainties must be considered, such as the material type and form, the sampling procedure, and the measurement method. According to theory and experience, the inspectorate detection... 

As in every test, the results from mechanical properties tests on composite materials depend on chance variations in a number of influencing factors. The stochastic character of measurement results requires the use of statistical evaluation methods [15]. 

The estimated Fvalue has to be compared with the quantile of the F-distribution, Fi a>v, the tables of which can be found in textbooks of statistics (e.g., Hald [I960] Neave [1981] Dixon and Massey [1983] Graf et al. [1987] Sachs [1992]). The influence of the factor a is significant when F exceeds Ft a>v. In case of unbalanced experiments the different size of measurement series and, therefore, degrees of freedom have to be considered as a result of which both the evaluation scheme and the variance decomposition become more complicated (see Dixon and Massey [1983] Graf et al. [1987]). 

Dunkle s Syllabus (1957-1958) Shock Tube Studies in Detonation (pp 123-25) Determination of Pressure Effect (144-45) Geometrical and Mechanical Influences (145-48) Statistical Effects of Sensitivity Discussion on Impact Sensitivity Evaluation (148-49) Pressure in the Detonation Head (175) Temperature of Detonation (176) Charge Density, Porosity, and Granulation (Factors Affecting the Detonation Process) (212-16) Heats of Explosion and Detonation (243-46) Pressures of Detonation (262-63) A brief description of Trauzl Block Test, Sand Test, Plate Dent Test, Fragmentation Test, Hess Test (Lead Block Crushing Test), Kast Test (Copper Cylinder Compression Test), Quinan. Test and Hop-kinson Pressure Bar Test (264-67) Detonation Calorimeters (277-78) Measurements... 

One can conclude that ANOVA can be a very useful test for evaluating both systematic and random errors in data, and is a useful addition to the basic statistical tests mentioned previously in this chapter. It is important to note, however, there are other factors that can greatly influence the outcome of any statistical test, as any result obtained is directly affected by the quality of the data used. It is therefore important to assess the quality of the input data, to ensure that it is free from errors. One of the most commonly encountered errors is that of outliers. 

Although considerable progress has been made in the metabolic profile approach, a number of problems remain to be overcome. Many of these centre around the fluctuations in component composition, not from metabolic disorders, but brought about by other influences. These are principally due to diet and the metabolic variations in individuals in relation to activity. Drugs can also affect the excretion levels of compounds, in addition to the production of their own metabolites. These factors all make quantitative data difficult to obtain and evaluate. Careful statistical analysis of the results are necessary and a population of 500 subjects, grouped in age and sex, has been studied with a view to obtaining a suitable data base for urinary organic acids [370]. 

Many of these trial-dependent factors are ultimately evaluated relative to their influence on the statistical power of the study. Additionally, the study data analysis method(s) to be employed may also be a consideration for the simulation. 

Additionally to the measurement as described above, a free selected number (mostly 3-11) of channels is measured (scanned) and the highest value in this window is taken as Neff. This evaluation procedure can be well applied to concentrations clear above the detection limit (by at least a factor of 100), otherwise the counting statistic will influence the "highest value" and also lead to a higher relative standard deviation (3-5%). On the other hand, the advantage is a relatively fast measurement and therefore a low sample consumption. Especially in case of biological and medical samples, when only small... 

To select the most important factors that influence a given analytical problem based on statistical approaches of experimental design as well as on evaluating the factor effects and their interactions by means of statistical tests... 

The response surface methodology (RSM) is a combination of mathematical and statistical techniques used to evaluate the relationship between a set of eontrollable experimental factors and observed results. This optimization proeess is used in situations where several input variables influence some output variables (responses) of the system. The main goal of RSM is to optimize the response, whieh is influenced by several independent variables, with minimum number of experiments. The central composite design (CCD) is the most common type of seeond-order designs that used in RSM and is appropriate for fitting a quadratic surface [26,27]. 

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