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.

In this research، the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394، which is compiled from reliable economic data bases of country. Independent variables are: net foreign assets of the banking system (billion rials)، net debt of the public sector to the banking system (billion rials)، liquidity in terms of its constituent parts (billion rials)، rate of dollar (rials)، economic participation rate، average inflation rate، The average annual interest rate on state-owned banks، the percentage of jobseekers (65-15). The results indicate that there is a negative and significant relationship between unemployment rate and average inflation rate and economic participation rate، while the net debt of the public sector has had a positive and significant relationship with the banking system and unemployment rate. The greatest negative effects on unemployment rate are the rate of economic participation and the greatest positive impact on the unemployment rate is the net debt of the public sector to the banking system.

Estimation of wave velocities is very important for designing geotechnical structures and modeling deep drillings. The purpose of this study is to estimate shear wave velocity (Vs) using Gaussian process regression (GPR), multilayer perceptron artificial neural network (MLP-ANN) and Multivariate linear regression (MVLR) methods. In order to carry out this study, 14 rock blocks were prepared from the northwest of Damavand city and after being transferred to the laboratory, cores were extracted from them. In order to develop a predictive model, point load index, compressional wave velocity (Vp), porosity and density tests were performed on 61 rock core samples. Point load index, Vp, porosity and density were used as input parameters of models to predict Vs. The results of lithological studies showed that the studied sandstones are feldspathic litharnite and litharnite. The results showed that the ratio of Vp to Vs is equal to 1.70 on average. The results of the MLP-ANN showed that the highest accuracy of the models was obtained by using the Levenberg-Marquardt training algorithm. The most accurate models were obtained using this algorithm to estimate the Vs in neuron number 2 (optimal neuron). The GPR, MLP-ANN and MVLR predicted Vs with correlation coefficients of 0.97, 0.96 and 0.95, respectively. GPR method showed better performance in predicting Vs than other methods.

Objective: The current study was conducted to predict the dimensions of positive attitudes towards drugs in senior high school students based on individual and family characteristics.Method: All the senior high school students of Tehran city constituted the statistical population of this study, among whom the number of 400 participants was selected by multi-stage cluster sampling method. These participants responded to Attitude to Drugs and Stimulants Scale, Scale of Secure-Insecure Levels of Family, Parenting Styles Scale, and Index of Brother and Sister Relations. This study was a correlational one wherein the obtained were analyzed using Multivariate linear regression and multiple linear regression.Results: The results show that age, family relationships, and parenting styles can predict the triple dimensional attitudes to drugs. In return, students' general attitude to drugs will be predictable from age, father’s education, family relationships, emotional atmosphere of families.Conclusion: Precise information about the individual and family characteristics that can predict positive attitudes to drugs is accessible when the three dimensions of attitudes to drugs are analyzed separately.

In this paper, we obtain the generalized least square, restricted generalized least square and shrinkage estimators for the regression vector parameter assuming that the errors have Multivariate t distribution. Also we calculate their quadratic risks and propose the dominance order of the underlying estimators.