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.

In the current study, the qualitative status of potable well water was assessed using the groundwater quality index during a course of 4 years (2014-2017). This study was carried out with an aim to monitor the drinking water resources from 12 potable wells on the MULTIVARIATE ANALYSIS basis and for determination of groundwater quality index, the following 13 physicochemical parameters including electrical conductivity, total dissolved solids, pH, total hardness, potassium, fluoride, bicarbonate, chloride, calcium, magnesium, sulphate, and nitrate were used. On the basis of Piper diagram, the results revealed that the type and faces of samples were chloride-sodic and bicarbonate-sodic respectively. Groundwater quality index level in the potable well water of case study area was 42. 89 to 56. 58 and zone water was in the good and medium range. Besides, 66. 7% of the wells were in the good range and 33. 3% of wells were in the medium range of water quality index. In this study, potassium and fluoride level in all the zone wells was lower than the ideal level and the electrical conductivity, total dissolved solids, sodium, magnesium and sulphate in all the wells was higher than the ideal range for drinking purposes. Based on this study results, the potable water quality of most of the study area wells generally in 2017 vis-à-vis 2014 had reduced and its main reason was the presence of geology formations, agricultural runoffs and absorbing wells in this zone.

According to the nature of their activities, banks are exposed to various types of risks. Hence, risk management is at the heart of financial institutions management. In this study, we intend to summarize the information content of bank financial statements on diverse risks faced by banks and then determine how stock markets react to bank's risk management behavior. The methodology used in this study is the Principal Component ANALYSIS (PCA) and Discriminant ANALYSIS (DA). In this research, we evaluate the status of risk management in listed banks on Tehran Stock Exchange through financial statements ANALYSIS and then investigate its relationship with banks' stock returns to determine whether capital market participants take the status of risk management into account in their pricing decisions or not. The results show that provisions taken by banks have a meaningful relationship with the banks' stock returns. However, capital adequacy, net interest margin, and net margin of non-interest income have no significant relationship with stock returns.