The statistical analysis of multivariate serological frequency data

2005 ◽  
Vol 67 (6) ◽  
pp. 1303-1313 ◽  
Author(s):  
R REYMENT
Keyword(s):  
Statistical Analysis ◽  
Frequency Data

Was This in Your Statistics Textbook? IV. Frequency Data

1989 ◽  
Vol 25 (1) ◽  
pp. 11-25
Author(s):  
D. J. Finney
Keyword(s):  
Statistical Analysis ◽  
Contingency Tables ◽  
The Other ◽  
Frequency Data ◽  
Significance Tests ◽  
Test Statistic ◽  
Jumping To Conclusions ◽  
Proper Role ◽  
Measure Of Association

SUMMARYObservations that are frequencies rather than measurements often call for special types of statistical analysis. This paper comments on circumstances in which methods for one type of data can sensibly be used for the other. A section on two-way contingency tables emphasizes the proper role of χ2 a test statistic but not a measure of association; it mentions the distinction between one-tail and two-tail significance tests and reminds the reader of dangers. Multiway tables bring new complications, and the problems of interactions when additional classificatory factors are explicit or hidden are discussed at some length. A brief outline attempts to show how probit, logit, and similar techniques are related to the analysis of contingency tables. Finally, three unusual examples are described as illustrations of the care that is needed to avoid jumping to conclusions on how frequency data should be analysed.

scholarly journals A Statistical Analysis Of CoV-19 Positive Test Frequency Data Indicates A Need For Greater Attention To CoV-19 Test Quality And Pre-Wuhan Cov-19 Prevalence

2020 ◽  
Author(s):  
James L. Sherley
Keyword(s):  
Statistical Analysis ◽  
Epidemiological Data ◽  
Positive Test ◽  
Human Populations ◽  
Frequency Data ◽  
Test Frequency ◽  
Test Quality ◽  
Public Response ◽  
History Of ◽  
Improved Accuracy

AbstractIncreased attention to analysis of SARS-CoV-2 (CoV-19) positive test frequency data is essential for achievement of better knowledge of the natural history of the virus in human populations, improved accuracy of CoV-19 epidemiological data, and development of public response policies that are better crafted to address the current CoV-19-induced global crisis. A statistical analysis of currently available positive test frequency data reveals a surprisingly uniform relationship between the number of CoV-19 test performed and the number of positive tests obtained. The uniformity is particularly striking for United States CoV-19 test data. Such observations warrant closer evaluation of other factors, besides virus spread, that may also contribute to the nature of the coronavirus pandemic. These include indigenous CoV-19 and the quality of CoV-19 testing.

Seabed characterization from statistical analysis of high‐frequency data

2001 ◽  
Vol 110 (5) ◽  
pp. 2661-2661
Author(s):  
Samantha J. Dugelay ◽  
Richard J. Brothers ◽  
Gary J. Heald
Keyword(s):  
Statistical Analysis ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data

scholarly journals Statistical analysis of lacZ mutant frequency data from MutaTMMouse mutagenicity assays

Mutagenesis ◽  
1998 ◽  
Vol 13 (3) ◽  
pp. 249-255 ◽  
Author(s):  
Karen Y. Fung ◽  
George R. Douglas ◽  
Daniel Krewski
Keyword(s):  
Statistical Analysis ◽  
Frequency Data ◽  
Mutant Frequency

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.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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