Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 6 Articles
Good estimates of the reliability of a system make use of test data and expert knowledge at all available levels. Furthermore, by\r\nintegrating all these information sources, one can determine how best to allocate scarce testing resources to reduce uncertainty.\r\nBoth of these goals are facilitated by modern Bayesian computational methods. We demonstrate these tools using examples that\r\nwere previously solvable only through the use of ingenious approximations, and employ genetic algorithms to guide resource\r\nallocation....
Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical\r\nmethods. In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the\r\nanalysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point\r\nto quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is\r\nshown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling\r\nmatrices. For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process\r\ndefining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such\r\nkind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of\r\nthe chosen model specification is outlined....
We assume that the operator is interested in monitoring a multinomial process. In this case the items are classified into (k + 1)\r\nordered distinct and mutually exclusive defect categories. The first category is used to classify the conforming defect-free items,\r\nwhile the remaining k categories are used to classify the nonconforming items in k defect grades, with increasing degrees of\r\nnonconformity. Usually the process is said to be capable if the overall proportion of nonconforming items is very small and remains\r\nlow, or declines over time. Nevertheless, since we classify the nonconforming items into k distinct defect grades, the operator\r\ncan also evaluate the overall level of defectiveness. This quality parameter depends on the k defect categories. Furthermore, we\r\nare interested in evaluating, over time, the proportion of nonconforming items in each category as well as the overall level of\r\ndefectiveness. To achieve this goal, we propose (i) a normalized index that can be used to evaluate the capability of the process\r\nin terms of the overall level of defectiveness, and (ii) a two-sided Shewhart-type multivariate control chart to monitor the overall\r\nproportion of nonconforming items and the corresponding defectiveness level....
Modern production methods demand the synchronous multicharacteristic optimization of goods. There is a need to diversify a\r\nbasic product to the importance placed on its individual quality components by a wide spectrum of concerned customers. This\r\nwork shows how the super-ranking concept may be utilized taking into account relative weights among the implicated responses.\r\nThe theoretical development is focused on the difficult situation where the optimization is attempted through unreplicated and\r\nsaturated fractional factorial designs. A nested super-ranking scheme is devised to accommodate a dual weight assignment,\r\nfirst by setting up a single consolidated response per implicated customer and then, in a subsequent step, by incorporating a\r\ncustomer importance rating thus rendering an overall single master response. A demonstration of the proposed method on a\r\npragmatic problem arising in aluminum milling involves optimization due to seven controlling factors concurrently influencing\r\nnine product responses modulated by six preference ratings set by a given customer base, respectively. Key benefits of this method\r\nare the offered ease of intermixing numerical and categorical data in mainstream multiresponse optimization problems, and\r\nkeeping customer preferences in perspective through economical, short-cycle screening while relaxing stringent data normality\r\nand possible multidistributional effects among the implicated quality characteristics....
This study addresses spatial effects by applying spatial analysis in studying whether household economic status (HES) is related to\r\nhealth across governorates in Iraq. The aim is to assess variation in health and whether this variation is accounted for by variation\r\nin HES. A spatial univariate and bivariate autocorrelation measures were applied to cross-sectional data from census conducted in\r\n2004. The hypothesis of spatial clustering for HES was confirmed by a positive global Moran�s I of 0.28 with P = 0.010, while for\r\nhealth was not confirmed by a negative global Moran�s I of -0.03. Based on local Moran�s Ii, two and seven significant clusters in\r\nhealth and in HES were found respectively. Bivariate spatial correlation between health and HES wasn�t found significant (Ixy =\r\n-0.08) with P = 0.80. In conclusion, geographical variation was found in each of health and HES. Based on visual inspection, the\r\npatterns formed by governorates with lowest health and those with lowest HES were partly identical. However, this study cannot\r\nsupport the hypothesis that variation in HES may spatially explain variation in health. Further research is needed to understand\r\nmechanisms underlying the influence of neighbourhood context....
A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance\r\nmetrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and\r\nattractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the\r\nheteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare\r\nit with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the\r\nmodel are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and\r\nits associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously\r\nmonitored by an automated system that flags components that are suspected of unusual degradation patterns....
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