3 resultados para Descriptive Analysis

em Aston University Research Archive


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Discriminant analysis (also known as discriminant function analysis or multiple discriminant analysis) is a multivariate statistical method of testing the degree to which two or more populations may overlap with each other. It was devised independently by several statisticians including Fisher, Mahalanobis, and Hotelling ). The technique has several possible applications in Microbiology. First, in a clinical microbiological setting, if two different infectious diseases were defined by a number of clinical and pathological variables, it may be useful to decide which measurements were the most effective at distinguishing between the two diseases. Second, in an environmental microbiological setting, the technique could be used to study the relationships between different populations, e.g., to what extent do the properties of soils in which the bacterium Azotobacter is found differ from those in which it is absent? Third, the method can be used as a multivariate ‘t’ test , i.e., given a number of related measurements on two groups, the analysis can provide a single test of the hypothesis that the two populations have the same means for all the variables studied. This statnote describes one of the most popular applications of discriminant analysis in identifying the descriptive variables that can distinguish between two populations.

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PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

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The initial aim of this research was to investigate the application of expert Systems, or Knowledge Base Systems technology to the automated synthesis of Hazard and Operability Studies. Due to the generic nature of Fault Analysis problems and the way in which Knowledge Base Systems work, this goal has evolved into a consideration of automated support for Fault Analysis in general, covering HAZOP, Fault Tree Analysis, FMEA and Fault Diagnosis in the Process Industries. This thesis described a proposed architecture for such an Expert System. The purpose of the System is to produce a descriptive model of faults and fault propagation from a description of the physical structure of the plant. From these descriptive models, the desired Fault Analysis may be produced. The way in which this is done reflects the complexity of the problem which, in principle, encompasses the whole of the discipline of Process Engineering. An attempt is made to incorporate the perceived method that an expert uses to solve the problem; keywords, heuristics and guidelines from techniques such as HAZOP and Fault Tree Synthesis are used. In a truly Expert System, the performance of the system is strongly dependent on the high quality of the knowledge that is incorporated. This expert knowledge takes the form of heuristics or rules of thumb which are used in problem solving. This research has shown that, for the application of fault analysis heuristics, it is necessary to have a representation of the details of fault propagation within a process. This helps to ensure the robustness of the system - a gradual rather than abrupt degradation at the boundaries of the domain knowledge.