Augmenting Hybrid Physics-Based Multivariate Analysis with the Alternating Conditional Expectations Approach to Optimize Permian Basin Well Performance

2018 ◽  
Author(s):  
Ely Lolon ◽  
Karn Agarwal ◽  
Michael Mayerhofer ◽  
Oladapo Oduba ◽  
Howard Melcher ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Well Performance ◽  
Permian Basin ◽  
Conditional Expectations

scholarly journals APPLICATION OF MULTIVARIATE ANALYSIS TO IDENTIFY DROUGHT TOLERANCE GENOTYPES OF MAIZE

2017 ◽  
Vol 48 (4) ◽  
Author(s):  
Ahmed & Kadhem
Keyword(s):  
Multivariate Analysis ◽  
Zea Mays L ◽  
Stress Conditions ◽  
Drought Indices ◽  
Well Performance ◽  
Spring Season ◽  
Potential Productivity ◽  
Drought Tolerant ◽  
Maize Genotypes ◽  
The Relationship

In order to identify maize (Zea mays L.) genotypes had tolerate  drought, Seven drought indices and 18 maize genotypes were used,  to determine the efficiency of theses indices to identify tolerant and susceptible genotypes, Also interpretation the relationship between these indices using  principle component , cluster of multivariate analysis and rank-sum method.  The results indicated that MP, GMP, and STI were positively and significant correlated with Yp and Ys. Therefore, these indices have the potential to identify genotypes own well performance to stress and non-stress  conditions. Screening drought tolerant genotypes using these indices discriminating   G8 (Zm32*Zm60) G2 (Zm69*Zm49), G  (Zm60*Zm61), and G11 (Zm49*Zm60) in spring season; and G8 (Zm32*Zm60) G2(Zm69*Zm49 ),  G15 (Zm49*Zm19)  and  G18 (Zm60*Zm51) for autumn season as tolerant genotypes and have a higher potential productivity under stress and non-stress conditions.

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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