Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Measurement uncertainty (Statistics)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of "measurement uncertainty" has been introduced in 1995 by the "Guide to the expression of uncertainty in measurement" (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result.

Measurement uncertainty (Statistics) --- Measurement

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Measurement uncertainty (Statistics)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in estimation of a linear model with a conditionally heteroskedastic disturbance.

Estimation theory. --- Measurement uncertainty (Statistics)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Measurement uncertainty is an important component of modern materials analysis: it indicates the boundaries within which the test results can be trusted. Such results are necessary for understanding of, for example, material and product tolerances and lifetimes, vital for plastic product reliability and safety. Determination of measurement uncertainty is normally quite laborious, but this book shows how the available interlaboratory test data for plastics can be used to calculate measurement uncertainty much more simply. It contains many interlaboratory test results in the fields of thermoanalysis, molar mass determination, and quantitative analysis of the composition of material, presented in tables and graphical charts, discussed in the text, and elaborated by practical examples. In addition to the evaluation by means of the presented data (top-down approach), the relationship to the bottom-up approach specified in the Guide to the Expression of Uncertainty in Measurement (GUM) is explained based on an example. Further sections deal with sampling, and the issue of whether or not the difference between analytical results is significant.

Plastics --- Analysis. --- Safety measures. --- Measurement uncertainty (Statistics)