Robust Measure
Robust Measure - Related Videos
06:32
A Simple, Low-cost, and Robust System to Measure the Volume of Hydrogen Evolved by Chemical Reactions with Aqueous Solutions
0 Views •
Cited by 16 •
2016
The study of methods to generate on-demand hydrogen for fuel cells continues to grow in importance. However, systems to measure hydrogen evolution from the reaction of chemicals with water can be complicated and expensive. This article details a simple, low-cost, and robust method to measure the evolution of hydrogen gas.
12:16
Robust 3D DNA FISH Using Directly Labeled Probes
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Cited by 45 •
2013
We describe a robust and versatile protocol for analyzing nuclear architecture by 3D DNA FISH using directly labeled fluorescent probes.
07:41
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
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Cited by 2 •
2022
This article describes a workflow of techniques employed for testing novel candidate mediators of melanoma metastasis and their mechanism(s) of action.
14:53
Techniques for the Evolution of Robust Pentose-fermenting Yeast for Bioconversion of Lignocellulose to Ethanol
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Cited by 4 •
2016
Adaptive evolution and isolation techniques are described and demonstrated to yield derivatives of Scheffersomyces stipitis strain NRRL Y-7124 that are able to rapidly consume hexose and pentose mixed sugars in enzyme saccharified undetoxified hydrolyzates and to accumulate over 40 g/L ethanol.
10:47
Manufacturing Of Robust Natural Fiber Preforms Utilizing Bacterial Cellulose as Binder
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Cited by 24 •
2014
We present a novel method of manufacturing rigid and robust short natural fiber preforms using a papermaking process. Bacterial cellulose acts simultaneously as the binder for the loose fibers and provides rigidity to the fiber preforms. These preforms can be infused with a resin to produce truly green hierarchical composites.
FAQs
Its influence is limited by using resistant summaries rather than allowing extreme observations to dominate the result. The median provides a stable description of location, while resistant measures of spread and association reduce the effect of unusual values. This makes the analysis more reliable when data contain contamination, measurement errors, or small departures from model assumptions.
The median is less affected by extreme values than a summary that can be pulled strongly by a few observations. It therefore gives a more stable indication of location when the data include outliers or contamination. This resistance helps analysts describe the central pattern without treating unusual measurements as representative of the entire dataset.
The median absolute deviation is a resistant summary used to describe spread. Because it is based on the median, it can provide a more stable view of variability when extreme observations are present. Pairing it with a resistant location measure helps analysts assess both the center and dispersion of a dataset without letting a few unusual values dominate.
They provide summaries that remain relatively stable when isolated observations are inaccurate or unusual. Analysts can compare these resistant descriptions with the apparent pattern in the full dataset, helping determine whether a result persists beyond potentially erroneous values. This is especially useful when exploratory analysis must separate genuine structure from effects caused by measurement problems.
Selection begins with the feature being summarized: location, spread, or association. A median or trimmed mean can provide a resistant location summary, while the median absolute deviation addresses spread. Choosing the measure according to the analytical goal produces a clearer description and helps prevent unusual observations from controlling the interpretation.
They are especially valuable for real-world datasets in which outliers, contamination, or small departures from model assumptions may affect ordinary summaries. In exploratory data analysis, they support stable descriptions of the data before researchers assess broader patterns. Their use helps reveal whether conclusions reflect the dataset generally or only a few unusual observations.
In quality control, resistant summaries can provide stable descriptions despite unusual measurements, helping identify whether apparent changes reflect broader data behavior or isolated problems. During model assessment, they help researchers judge patterns without allowing a few observations to dominate. These uses make the measures relevant when data quality and assumption departures may influence conclusions.