Introduction and purpose: Despite recent efforts to identify microplastics in the aquatic environment worldwide, identifying the various sources of its release remains a challenging task. Understanding and identifying the different sources of aquatic pollution and the processes affecting them is essential for a comprehensive description of the quality of water resources. The aim of this study is therefore to introduce statistical methods to determine the sources of microplastics in aquatic environments. Methods: This review article first identifies the pathways of microplastic entry into the aquatic environment, followed by an examination of four commonly used Multivariate statistical methods: Principal Component analysis (PCA), Cluster analysis (CA), Hierarchical Cluster analysis (HCA) and Positive Matrix Factorization (PMF). Results: Multivariate statistical analysis can be used to determine different variables such as size, shape, color, and density of microplastics. It can also determine the sources of microplastics (domestic wastewater, industrial effluents, agricultural activities, surface runoff, air currents, etc.). It also identifies which variables have the greatest impact on pollution and suggests the best solutions to reduce pollution. Conclusion: the study of pollution based on Multivariate statistical analysis can provide important information on the main sources of microplastic pollution and the relative contribution of different sources in the aquatic environment, which can help to improve environmental management and reduce pollution.

Background & Aim: One of the basic assumptions in simple linear regression models is the statistical independence of observations. Sometimes this assumption is not true for study subject and consequently the use of general regression models may not be appropriate. In this case, one of the leading methods is the use of multilevel models. The present study utilizes Multivariate logistic regression model using a multilevel model to exhibit the chance of having elbow, wrist and knee disorders over the past year based on elbow, wrist and disorders during the past week. Methods & Materials: This study is a cross-sectional study that was carried out from April 2015 to May 2016 in Mobarakeh Steel Company, Isfahan. The study population includes 300 male employees of Mobarakeh Steel Company, with a mean age of 41. 40± 8. 17 years and an average working experience of 16. 0± 7. 66 years. Data were analyzed using SPSS (version 24) and MLwiN software. Results: Based on this study, results obtained from single variable and multivariable regression were different. Conclusion: Based on this study, it can be suggested that multivariable regression cause a better and more accurate deduction compared to single variable method.

Q-mode hierarchical cluster (HCA) and principal component analysis (PCA) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: June, September, and December, along the Ain Azel aquifer, Algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the aquifer scale. Hydrochemical data for 54 groundwater samples were subjected to Q-mode hierarchical cluster and principal component analysis. The study finds, from Q-mode HCA that there are three main hydrochemical facies namely the less saline water (group 1: Ca-Mg-HCO3), mixed water (group 2: Mg-Ca-HCO3-Cl) and blended water (group 3: Mg-Ca-Cl-HCO3). In principal component analysis, the first 4 factors explain 72.14% of the total variance, their loadings allowing the interpretation of hydrochemical processes that take place in the area. The results of this study clearly demonstrate the usefulness of Multivariate statistical analysis in hydrochemical.

Tobacco (Nicotiana tabaccum) is one of the valuable agricultural and industrial crops that there is little information about its variation. For studying genetic variation on the basis of morphological characteristics, a number of 100 exotic and endemic oriental tobacco genotypes were obtained from the germplasm collection of the Urmia Tobacco Research Center, Urmia, Iran, using simple lattice design with 2 replications. Eight traits include: stem height and diameter, leaf number per plot, leaf length and width, fresh and dry leaf weight and day to 50% flowering were examined. Principal component analysis could reduce the studied morphological traits to 5 components having 96% accumulative variance. In the first component, all traits (except stem height) showed positive significant correlations with. Cluster analysis using UPGMA method distinguished genotypes in 4 different groups. Maximum distance was between groups 1 and 4. Mean comparison revealed that genotypes (Trimph and Ohdaruma) belong to group 4 had the maximum value of most examined traits, therefore, they could be utilized as parents of crosses in breeding programs.

In this paper we construct a modeling for detection of banks which are experiencing serious problems. Sample and variable set of the study contains 30 banks of Iran during 2006-2014 and their financial ratios. Well known Multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant Logit and Probit models were estimated based on these characteristics. Results suggest that the model can be used as an analytical decision support tool in both on-site and off-site bank monitoring system to detect the banks which are experiencing serious problems.