A Multivariate Analysis of Groundwater Chemistry Data

2021 ◽  
pp. 167-172
Author(s):  
Ahmed Garba ◽  
Muhammad Mukhtar ◽  
Aishatu Hussein Santuraki
Keyword(s):  
Multivariate Analysis ◽  
Groundwater Chemistry ◽  
Chemistry Data

A Multivariate Statistical Analysis of Groundwater Chemistry Data in the Highest Bandama Basin at Tortiya (Northern Côte D’Ivoire)

2013 ◽  
Vol 1 (3) ◽  
pp. 72 ◽  
Author(s):  
Soro Tanina Drissa ◽  
Ahoussi Kouassi Ernest ◽  
Oga Yei Marie-Solange ◽  
Soro Gbombélé ◽  
Soro Nagnin
Keyword(s):  
Statistical Analysis ◽  
Cote D'ivoire ◽  
Multivariate Statistical Analysis ◽  
Côte D’Ivoire ◽  
Groundwater Chemistry ◽  
Multivariate Statistical ◽  
Cote D’Ivoire ◽  
Chemistry Data ◽  
Côte D'ivoire

Metabolomics of Medicinal Plants: The Importance of Multivariate Analysis of Analytical Chemistry Data

2010 ◽  
Vol 6 (3) ◽  
pp. 179-196 ◽  
Author(s):  
Taketo Okada ◽  
Farit Mochamad Afendi ◽  
Md. Altaf-Ul-Amin ◽  
Hiroki Takahashi ◽  
Kensuke Nakamura ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Multivariate Analysis ◽  
Medicinal Plants ◽  
Chemistry Data

scholarly journals Interpretation of groundwater chemistry data using multivariate statistical techniques

2016 ◽  
Vol 18 (3) ◽  
pp. 665-673 ◽  
Keyword(s):  
Groundwater Quality ◽  
Graphical Representation ◽  
Species Abundance ◽  
R Package ◽  
Groundwater Chemistry ◽  
Multivariate Statistical ◽  
Chemistry Data ◽  
Groundwater Samples ◽  
History Of ◽  
P Factor

<p>Hydrogeologists in general and hydrogeochemists in particular are commonly asked to collect and interpret groundwater chemistry data for determining the groundwater quality at a particular site. More often, this involves graphical representation of data and a comparison with the drinking water quality standards. However, public laws and regulations require rigorous and a comprehensive quantitative approach, including statistical analysis to interpret the groundwater chemistry data. The analysis might be helpful in identifying the contaminated sites.</p> <p>A total of 19 groundwater samples were collected from Wadi Bani Malik located 40 km to the east of Jeddah, Saudi Arabia. The area once known to be a dumping ground for untreated waste has now been partially remediated. To establish that the higher concentration of salts found in the Wadi is due to the sewage dumping, the data were compared with an adjacent Wadi Madsus that was not known to have any history of dumping. A distribution-free method of multivariate data analysis was employed to compare the variation in species abundance and composition among sampling sites.</p> <div> <p>Principal Coordinate Analysis indicates that some of the wells in both the wadis have same compositional trend and also some samples overlap within the same area. Analysis of similarity (ANOSIM) using R package vegan software indicates a strong correlation (R = 0.82) between Wadi Bani Malik and Wadi Madsus. The P factor calculated while performing the analysis suggests that sewage dumping significantly contaminated (P = 0.001) the groundwater quality of the Bani Malik area.</p> </div> <p>&nbsp;</p>

Principal Component Analyses of Groundwater Chemistry Data During Enhanced Bioremediation

2006 ◽  
Vol 169 (1-4) ◽  
pp. 395-411 ◽  
Author(s):  
O. Mrklas ◽  
L. R. Bentley ◽  
S. R. D. Lunn ◽  
A. Chu
Keyword(s):  
Principal Component ◽  
Groundwater Chemistry ◽  
Chemistry Data ◽  
Principal Component Analyses ◽  
Enhanced Bioremediation

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.

1197: Prognostic Value of Hemoglobin Change after Initiation of Androgen Deprivations Therapy for Newly Diagnosed Metastatic Prostate Cancer: A Multivariate Analysis of Swog 8894

2006 ◽  
Vol 175 (4S) ◽  
pp. 385-386
Author(s):  
Tomasz M. Beer ◽  
Bryan H. Goldman ◽  
Catherine M. Tangen ◽  
Lisa B. Bland ◽  
Maha Hussain ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Multivariate Analysis ◽  
Prognostic Value ◽  
Metastatic Prostate Cancer ◽  
Newly Diagnosed
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