Impact Induced Fires: Statistical Analysis of FARS and State Data Files (1978-2001)

2005 ◽  
Author(s):  
Keith Friedman ◽  
Elizabeth Holloway ◽  
Timothy A. Kenney
Keyword(s):  
Statistical Analysis ◽  
State Data ◽  
Data Files

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

1960-1995 National and State Data Files on Adolescent Fertility

1955 ◽  
Author(s):  
Kritin A. Moore ◽  
◽  
Nancy Snyder ◽  
Angela Romano ◽  
Laura Gitelson
Keyword(s):  
State Data ◽  
Data Files ◽  
Adolescent Fertility

Management of data files in preparation for statistical analysis

1991 ◽  
Vol 12 (5) ◽  
pp. 709
Author(s):  
Pauline Raiz ◽  
Margaret Frederick ◽  
Sandra Forman
Keyword(s):  
Statistical Analysis ◽  
Data Files

scholarly journals A statistical analysis of the COHERENT data and applications to new physics

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Peter B. Denton ◽  
Julia Gehrlein
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Confidence Regions ◽  
New Physics ◽  
Model Predictions ◽  
Careful Treatment ◽  
Background Data ◽  
Low Energies ◽  
Data Files ◽  
Nucleus Scattering

Abstract The observation of coherent elastic neutrino nucleus scattering (CEνNS) by the COHERENT collaboration in 2017 has opened a new window to both test Standard Model predictions at relatively low energies and probe new physics scenarios. Our investigations show, however, that a careful treatment of the statistical methods used to analyze the data is essential to derive correct constraints and bounds on new physics parameters. In this manuscript we perform a detailed analysis of the publicly available COHERENT CsI data making use of all available background data. We point out that Wilks’ theorem is not fulfilled in general and a calculation of the confidence regions via Monte Carlo simulations following a Feldman-Cousins procedure is necessary. As an example for the necessity of this approach to test new physics scenarios we quantify the allowed ranges for several scenarios with neutrino non-standard interactions. Furthermore, we provide accompanying code to enable an easy implementation of other new physics scenarios as well as data files of our results: https://github.com/JuliaGehrlein/7stats.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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