External metrics

A final dimension of a metric is the intended audience. Information may be disseminated to internal audiences such as employees, supervisors, facility management, senior executives, and the board of directors or to external audiences such as a trade association, regulatory agency, or the general public. Any metric that must be reported for regulatory compliance is, by definition, an external metric. Chapter 6 discusses in greater detail the identification of metrics for various external and internal audiences. 

Both internal and external metrics are valuable. Internal metrics provide information to those throughout the organization with the information needed to evaluate the progress and effectiveness of the process safety management system. External metrics allow outside stakeholders to evaluate the organization s performance and to hold those within the organization accountable for unacceptable performance, (See Chapter 7 for a discussion on accountability.)... 

Having turned the workpiece to the correct diameter, the following procedure should be followed. This procedure is for screw-cutting a right-hand external metric thread on a machine having a metric leadscrew. 

In (Nagappan et al. 2007) an approach for early estimation of post-release field quality based on a set of easy-to-measure, in-process, internal, static unit test metrics is suggested. To do this, a metric suite called the Software Testing and Rehahility Early Warning for Java (STREW-J) have heen created. This metric suite consists of nine constituent metric ratios which are used to huild a regression model to estimate the post-release field quahty in terms of the external metric trouble reports per thousand hues of code (TRs/KLOC). 

With web-based technologies now accelerating it is becoming imperative to rethink the selection and implementation of the external metrics. This shift is not only in the measurement criteria but also in the mind-set of business practices. Collaboration requires a capacity to work in association, sometimes, with an enemy and does not achieve its business success at the competitor s expense. Table 18.3, adapted from Basu (2001), summarizes some specific areas where performance criteria have shifted along with changes from the enterprise-centric business to a collaborative supply chain. 

External metrics are defined in relation to running software. In what concerns GUIs, external metrics can be used as usability indicators. They are often associated with the following attributes (Nielsen, 1993) ... 

Considering that the models generated by the reverse engineering process are representations of the interaction between users and system, this research explored how metrics defined over those models can be used to obtain relevant information about the interaction. This means that the approach enable to analyse the quality of the user interface, from the users perspective, without having to resort to external metrics which would imply testing the system with real users, with all the costs that the process carries. 

In the case of health effects, other methods than stated or revealed preference methods are often used to estimate the impact of externalities and valuating the human health damages. Both productivity losses and costs for hospital admissions or other hospital-related activities are used to monetize health effects. Of special importance for the valuation of health effects are the metrics Value of a Statistical Life / Value of Prevented Fatality (VSL, VOSL or VPF) and Value of a Life Year Lost (VOLY). 

Because of the nature of the scientific method. Metrics is an indispensable tool of scientific research. It can provide rigorous indices of the internal consistency and the predictive power of "accepted knowledge" about study systems. Thus, it can aid with theory testing. It can also provide rigorous indices of the strength of correlations between the attributes of the study system and the external factors that might influence it. Thus, it can assist with statistical hypothesis formulation and testing. 

Root Mean Square Error of Prediction (RMSEP) Plot (Model Diagnostic) Prediction error is a useful metric for selecting the optimum number of factors to include in the model. This is because the models are most often used to predict the concentrations in future unknown samples. There are two approaches for generating a validation set for estimating the prediction error internal validation (i.e., cross-validation with the calibration data), or external validation (i.e., perform prediction on a separate validation set). Samples are usually at a premium, and so we most often use a cross- validation approach. 

Cosmology is based on the assumption that matter in the early stage of evolution of the Universe was of extraordinarily high density. From this, in 1966, Ya.B. and I. D. Novikov came to the conclusion that the generation of small black holes was possible in the early stages of evolution. Finally, it was shown in a very general form that the collapse of any nonsymmetrical object leads to the creation of an external observable metric which is wholly determined by conserved quantities [55] (with A. G. Doroshkevich and I. D. Novikov). 

The multivariate tools typically used for the NIR-CI analysis of pharmaceutical products fall into two main categories pattern recognition techniques and factor-based chemo-metric analysis methods. Pattern recognition algorithms such as spectral correlation or Euclidian distance calculations basically determine the similarity of a sample spectrum to a reference spectrum. These tools are especially useful for images where the individual pixels yield relatively unmixed spectra. These techniques can be used to quickly define spatial distributions of known materials based on external reference spectra. Alternatively, they can be used with internal references to locate and classify regions with similar spectral response. 

However, the easiest way is not always the best. Depending on the business model, key factors for success can differ considerably - especially along the dimensions of performance metrics. These differences need to be taken into account project attrition/win rate, for example, is an appropriate performance metric for a people-intensive applications developer with several small local subsidiaries. A global new product developer with low overall headcount but high external R D spend should, instead, be measured by the value of its product pipeline and the intellectual property owned. Given the different risk profiles of those two businesses, their ROCE targets, too, should be individually set. 

When appropriately validated and understood, biomarkers present unique advantages as tools for exposure assessment (Gundert-Remy et al, 2003). Biomarkers provide indices of absorbed dose that account for all routes and integrate over a variety of sources of exposure (IPCS, 1993, 2001a). Certain biomarkers can be used to represent past exposure (e.g. lead in bone), recent exposure (e.g. arsenic in urine), and even future target tissue doses (e.g. pesticides in adipose tissue). Once absorbed dose is determined using biomarkers, the line has been crossed between external exposure and the dose metrics that reflect the pharmacokinetics and toxicokinetics of an agent (see section 5.3.3). 

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