Metallurgical Evaluation of the Leaching Behavior of Copper–Cobalt-bearing Ores by the Principal Component Analysis Approach: Case Study of the DRC Copperbelt Ore Deposits

2021 ◽  
Author(s):  
Bienvenu Mbuya ◽  
Patrick Ntakamusthi ◽  
Méschac-Bill Kime ◽  
Léon Zeka ◽  
Guy Nkulu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Ore Deposits ◽  
Analysis Approach ◽  
Leaching Behavior ◽  
Metallurgical Evaluation

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

Component Analysis: An Aid to the Interpretation of Data

1969 ◽  
Vol 5 (2) ◽  
pp. 151-164 ◽  
Author(s):  
D. A. Holland
Keyword(s):  
Principal Component Analysis ◽  
Statistical Study ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Functions ◽  
Analysis Approach ◽  
Specific Technique ◽  
Mathematical Technique ◽  
Biological Concepts ◽  
General Component

SummaryPrincipal component analysis is a mathematical technique for summarizing a set of related measurements as a set of derived variates, frequently fewer in number, which are definable as independent linear functions of the original measurements. Consideration of their mathematical nature shows that they do not, themselves, necessarily correspond to sensible biological concepts, though they are more amenable to statistical study than the original measurements. Further, by assessing the extent to which they are in accordance with biological hypotheses, or with the results of other, similar, analyses, they can be transformed into other linear functions which are meaningful in the biological sense, or consistent with other results. Thus the specific technique of principal component analysis is developed into a more general component analysis approach. With proper regard for the purpose the analysis is intended to serve and for the mathematical restrictions involved, this approach can lead to a useful condensation of a mass of data, a better under-standing of the observed individuals as entities rather than collections of isolated measurements, and to the formulation of new hypotheses for subsequent examination.

scholarly journals Primary components in shaping koperasi masjid’s participation level: Employing a principle component analysis approach

2018 ◽  
Vol 31 (3) ◽  
pp. 318
Author(s):  
Fitriani Fitriani ◽  
Sudiyo Sudiyo ◽  
Dayang Berliana ◽  
Evi Yuniarti
Keyword(s):  
Factor Analysis ◽  
Strong Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Primary Factor ◽  
Analysis Approach ◽  
Second Primary ◽  
Study Approach ◽  
Participation Level

Koperasi masjid present themselves as a part of muamalah through Sharia economic facilitation. This study was conducted to analyse the cooperative members’ participation level in relation to the implementation of a cooperative business program. A survey using a case study approach was conducted at koperasi Masjid Hafshotul Iman, Masjid Taqwa and Masjid Darul HikmahThe survey employed a purposive sampling method with the participation of the cooperative’s management (chairman, secretary, and treasurer) and the masjid’s members. Ten respondents were purposively chosen from each mosque and a total 30 respondents were acquired. The distributed questionnaire was a closed one, measured with a Likert scale. The results were analysed using factor analysis and the Principal Component Analysis approach (PCA). Based on the results of the factor analysis, the members’ participation level in cooperative service can be classified into three primary factors. Some of the variables displayed a strong correlation with Factor 1, namely motivation, management performance, RAT performance, and supervisor performance. The second primary factor was formed by activeness in participating in cooperative activities. Lastly, the third primary factor encompassed three variables with a strong correlation, namely age, education, and membership duration.

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