scholarly journals Çok Değişkenli İstatistiksel Analizler ile Akkaya Gölü Rezervuar Topraklarındaki Ağır Metallerin Veri Analizi

2021 ◽  
Vol 9 (1) ◽  
pp. 249-257
Author(s):  
Fusun Yalçın
Keyword(s):  
Heavy Metals ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster ◽  
Multivariate Statistical ◽  
High Positive Correlation ◽  
Agricultural Areas ◽  
Chemical Contents

Data analysis is important in determining the origin of heavy metals accumulated in reservoir areas of lakes built for irrigation and understanding their toxic effects around agricultural areas. The aim of the study is to determine the behaviors of the heavy metals detected, the relationships among them, and to explain the possible origin of these metals by using multivariate statistical analyzes in the chemical contents of the soils in the Akkaya Lake reservoir area. The abundance of chemical analysis contents is listed as Mg> Al> Fe> S> Ti> Zn> V> As> Cu> Ni> Pb> Co> Mo> Sn> Cd> Hg. There is a high positive correlation between Fe and Mg, Si, K, Co, V, Cu, As, Ni, Zn and Pb. According to factor analysis, the total variance is 72.080 (% cumulative), divided into 3 (tree) factors. There were 3 groups according to the hierarchical cluster analysis and 4 groups according to the cluster analysis of the elements. It is understood that these groups offer similar characteristics among themselves. Multivariate statistical analysis was successful for this study.

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

scholarly journals The Social Profile of Students in Basic General Education in Ecuador: A Data Analysis

2017 ◽  
Vol 10 (6) ◽  
pp. 51
Author(s):  
Olga Elizabeth Minchala Buri ◽  
Efstathios Stefos
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Statistical Analysis ◽  
General Education ◽  
National Survey ◽  
Hierarchical Cluster Analysis ◽  
Descriptive Analysis ◽  
Hierarchical Cluster ◽  
Factorial Analysis ◽  
The Social

The objective of this study is to examine the social profile of students who are enrolled in Basic General Education in Ecuador. Both a descriptive and multidimensional statistical analysis was carried out based on the data provided by the National Survey of Employment, Unemployment and Underemployment in 2015. The descriptive analysis shows the frequency and percentages of variables used in the investigation, and the multidimensional statistical analysis demonstrates the principal and more important criteria of differentiation and classification among the clusters of students who were investigated. These methods involve factorial analysis of multiple correspondences which demonstrate criteria of differentiation and a hierarchical cluster analysis to define clusters of students according to their common traits.

Factor Analysis of Card Sort Data: An Alternative to Hierarchical Cluster Analysis

2005 ◽  
Vol 49 (5) ◽  
pp. 691-695 ◽  
Author(s):  
Miranda G. Capra
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Common Method ◽  
Card Sorting ◽  
Card Sort ◽  
Formal Statistical Analysis

Software and product designers use card sorting to understand item groups and relationships. In the usability community, a common method of formal statistical analysis for open card sort data is hierarchical cluster analysis, which results in a tree of the items sorted into distinct, nested clusters. Hierarchical cluster analysis is appropriate for highly structured settings, like software menus. However, many situations call for softer clusters, such as designing websites where multiple pages link to the same target page. Factor analysis summarizes the categories created in card sorts and generates clusters that can overlap. This paper explains how to prepare card sort data for statistical analysis, describes the results of factor analysis and how to interpret them, and discusses when hierarchical cluster analysis and factor analysis are appropriate.

Assessment of Heavy Metals Content in the Proximity of a Contaminated Site From Cluj-Napoca (Romania)

2020 ◽  
Vol 65 (2) ◽  
pp. 17-37
Author(s):  
Georgiana Grosu ◽  
◽  
Carmen Andreea Roba ◽  
Ramona Bălc ◽  
Maria Lucia Bizău-Cârstea ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sediment Contamination ◽  
Contaminated Site ◽  
Pollution Indices ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Water And Sediment ◽  
Physico Chemical

The present study was conducted in the proximity of a contaminated site from Cluj-Napoca city (Cluj County, Romania), where metal processing activities have been carried out for decades. Metal content and physico-chemical parameters were analyzed in soil, water and sediment samples, while organic matter (OM) and total organic carbon (TOC) was additionally analyzed for the soil samples. The sources of heavy metals were evaluated based on multivariate statistical analysis, while the soil and sediment contamination degree was assessed based on specific pollution indices. The calculated indices indicated a significant pollution with Cd and Pb, which may represent a risk if the area would become a residential area. Keywords: heavy metals, contaminated site, soil pollution indices, multivariate statistical analysis, Cluj-Napoca

scholarly journals Micro-SME Characterization From Financial Statements

2012 ◽  
Vol 11 (13) ◽  
pp. 1507
Author(s):  
Guillermo Ceballos-Santamaria ◽  
Juan-Jose Villanueva-Alvaro ◽  
Jose Mondejar-Jimenez
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Small Businesses ◽  
Financial Statements ◽  
Developed Countries ◽  
Financial Statement ◽  
Statistical Techniques ◽  
General Interest ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

In recent years, small businesses have created interest and research, because they represent the majority of the business fabric and account for over seventy per cent of jobs in developed countries. Governments of these countries share a general interest in knowing about Small and Medium-Sized Enterprises (SME). Based on this premise, the approach of this study is to characterize micro-SMEs in the province of Cuenca, Spain, by analysis of financial statements, specifically analyzing their structure in financial terms by use of univariate and multivariate statistical techniques allowing this kind of business in the province of Cuenca to be identified. The information used comes from the databases of SABI (Iberian Financial Statement Analysis Systems), DIRCE (Central Business Directory and CamerData, the database of the Chambers of Commerce. The statistical analysis is centered on a classic modal of exploratory factor analysis, and finally the main results arising from the study are presented.

scholarly journals Data Analysis of Beach Sands’ Chemical Analysis Using Multivariate Statistical Methods and Heavy Metal Distribution Maps: The Case of Moonlight Beach Sands, Kemer, Antalya, Turkey

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1538 ◽  
Author(s):  
Fusun Yalcin
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Data Analysis ◽  
Chemical Analysis ◽  
Statistical Methods ◽  
Risk Index ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Distribution Maps ◽  
Beach Sands

Multivariate statistical methods are widely used in several disciplines of fundamental sciences. In the present study, the data analysis of the chemical analysis of the sands of Moonlight Beach in the Kemer region was examined using multivariate statistical methods. This study consists of three parts. The multivariate statistical analysis tests were described in the first part, then the pollution indexes were studied in the second part. Finally, the distribution maps of the chemical analyses and pollution indexes were generated using the obtained data. The heavy metals were mostly observed in location K1, while they were sorted as follows based on their concentrations: Mg > Fe > Al > Ti > Sr > Mn > Cr > Ni > Zn > Zr > Cu > Rb. Also, strong positive correlations were found between Si, Fe, Al, K, Ti, P. According to the results of factor analysis, it was found that four factors explained 83.5% of the total variance. On the other hand, the coefficient of determination (R2) was calculated as 63.6% in the regression model. Each unit increase in the value of Ti leads to an increase of 0.022 units in the value of Si. Potential Ecological Risk Index analysis results (RI < 150) revealed that the study area had no risk. However, the locations around Moonlight Beach are under risk in terms of Enrichment Factor and Contamination Factor values. The index values of heavy metals in the anomaly maps and their densities were found to be successful; and higher densities were observed based on heavy metal anomalies.

Risk estimation and multivariate statistical analysis of the heavy metal content of drinking water samples

2018 ◽  
Vol 34 (10) ◽  
pp. 714-725
Author(s):  
Rajan Jakhu ◽  
Rohit Mehra
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Inductively Coupled Plasma ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Drinking Water Samples

Drinking water samples of Jaipur and Ajmer districts of Rajasthan, India, were collected and analyzed for the measurement of concentration of heavy metals. The purpose of this study was to determine the sources of the heavy metals in the drinking water. Inductively coupled plasma mass spectrometry was used for the determination of the heavy metal concentrations, and for the statistical analysis of the data, principal component analysis and cluster analysis were performed. It was observed from the results that with respect to WHO guidelines, the water samples of some locations exceeded the contamination levels for lead (Pb), selenium (Se), and mercury (Hg), and with reference to the EPA guidelines, the samples were determined unsuitable for drinking because of high concentrations of Pb and Hg. Using multivariate statistical analysis, we determined that copper, manganese, arsenic, Se, and Hg were of anthropogenic origin, while Pb, copper, and cadmium were of geogenic origin. The present study reports the dominance of the anthropogenic contributions over geogenics in the studied area. The sources of the anthropogenic contaminants need to be investigated in a future study.

Sign in / Sign up

Export Citation Format

Share Document