Comparison of undergraduate chemical engineering curricula between China and America universities based on statistical analysis

Detection of gross errors in chemical engineering measurements is studied, based on statistical analysis of redundant data. A method of simple evaluation of the gross error detection efficiency is presented. The construction of the power characteristics is illustrated by an example. The study complements the previous authors work on this subject, where the same problem was studied by stochastic simulation.

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Detection of gross errors in chemical engineering measurements

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19832614 ◽

1983 ◽

Vol 48 (9) ◽

pp. 2614-2626 ◽

Cited By ~ 1

Author(s):

František Madron

Keyword(s):

Statistical Analysis ◽

Chemical Engineering ◽

Large Error ◽

Measured Data ◽

Redundant Data ◽

Gross Error ◽

Error Of Measurement

Detection of gross errors in chemical engineering measurements is studied, based on statistical analysis of redundant data. The error of third type has been defined as the unacceptably large error of a quantity which is the aim of measurement, while the gross error of measurement is not detected. The method of classification of directly measured quantities into disjoint subsets is described according to possible effective detection of occurence of gross errors of measurements by statistical analysis of measured data.

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Statistical analysis of undergraduate chemical engineering curricula of United States of America universities: Trends and observations

Education for Chemical Engineers ◽

10.1016/j.ece.2017.04.002 ◽

2017 ◽

Vol 20 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Roman S. Voronov ◽

Sagnik Basuray ◽

Gordana Obuskovic ◽

Laurent Simon ◽

Robert B. Barat ◽

...

Keyword(s):

United States ◽

Statistical Analysis ◽

United States Of America ◽

Chemical Engineering

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010013064x ◽

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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