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Exploratory data analysis concepts

A concept related (but not identical) to resistance and exploratory data analysis is that of robustness. Robustness generally implies insensitivity to departures from assumptions surrounding an underlying model, such as normality. [Pg.909]

In the stand-alone DAP, the key essential feature is prespecification of the analysis in which the primary analysis variable(s) are defined and methods for dealing with anticipated problems are defined. A DAP differs from the concept of a SAP as defined by the ICH in one important aspect. Modeling is largely an exercise in exploratory data analysis. There are rarely specific hypotheses to be tested. Hence, any PopPK analysis cannot be described in the detail outlined by a SAP under ICH guidelines. Nevertheless, certain elements can be predefined and identified prior to conducting any analysis. But keep in mind that a DAP devoid of detail is essentially meaningless, whereas a DAP that is so detailed will inevitably force the analyst to deviate... [Pg.267]

This PhD follows statistician John Tukey s concept of Exploratory Data Analysis [12] the primary purpose of visualizing and exploring is to raise questions and gather insights about a large quantity of data. Unlike most statistical work, which evaluates a priori questions according to a model, exploration by information visualization has the potential to start analysis without assumptions, or open new perspectives on a previously-analyzed dataset. For these purposes, overviews of the whole network are crucial. [Pg.606]

The second part of the book—Chapters 9-12— presents some selected applications of chemometrics to different topics of interest in the field of food authentication and control. Chapter 9 deals with the application of chemometric methods to the analysis of hyperspectral images, that is, of those images where a complete spectrum is recorded at each of the pixels. After a description of the peculiar characteristics of images as data, a detailed discussion on the use of exploratory data analytical tools, calibration and classification methods is presented. The aim of Chapter 10 is to present an overview of the role of chemometrics in food traceability, starting from the characterisation of soils up to the classification and authentication of the final product. The discussion is accompanied by examples taken from the different ambits where chemometrics can be used for tracing and authenticating foodstuffs. Chapter 11 introduces NMR-based metabolomics as a potentially useful tool for food quality control. After a description of the bases of the metabolomics approach, examples of its application for authentication, identification of adulterations, control of the safety of use, and processing are presented and discussed. Finally, Chapter 12 introduces the concept of interval methods in chemometrics, both for data pretreatment and data analysis. The topics... [Pg.18]


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Exploratory analysis

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