Regarding the importance of faulting and its effect on the societies, specially the human beings’ lives, communities’ economy and sustainable development, this study is to collect the information about major earthquakes, moment magnitudes, seismic moments, earthquake depths, ruptures lengths and faults lengths. The experimental formulas governing these factors are studied and presented in the form of faulting experimental formulas. In this regard the data of Iran’s faulting catalog (the faults that have determined seismic activities and documented seismic parameters) and worldwide important earthquakes are collected and then regressed in several statistic steps. The data are analyzed using monovariable and multivariable statistical methods. Finally, the best linear and nonlinear functions are fitted applying the statistical software such as Datafit.

The concept of generalized order statistics (GOSs) was introduced as a unified approach to a variety of models of ordered random variables. The purpose of this paper is to investigate conditions on the underlying distribution functions and the parameters on which GOSs are based, to establish Shaked-Shanthikumar Multivariate dispersive ordering of GOSs from one sample and Khaledi-Kochar Multivariate dispersive ordering of GOSs from two samples. Some applications are also given.

The exact distribution of many applicable statistics could not be accessible in various statistical inference problems. To deal with such an issue in the large sample problem, an approach is to obtain the asymptotic distribution. In this article, we have expressed the asymptotic distribution of Multivariate statistics class approximated by averages based on the Taylor expansion. Then, the asymptotic distribution of an empirical Mahalanobis depth-based statistic is obtained, and the statistic is applied to test the scale difference between two Multivariate distributions. Simulation studies are carried out to explore the behavior of the asymptotic distribution of the test statistic. A real data example illustrating the use of the test is also presented.

The earth observation data sets were employed to study the land use/land cover change in study area from year 2000-2010. Vegetation, built-up area and agriculture classes had shown maximum changes. The lake water samples were analyzed, and further, Water Quality Index (WQI) was computed to categorize the lake water. The average value of WQI is 64.52, 52.23 and 42.45 in pre-monsoon, monsoon and post-monsoon seasons, respectively. Generally, pre-monsoon samples have higher number of polluted samples. Moreover, we applied the Multivariate statistical techniques for handling large and complex data sets in order to get better information about the lake water quality. Factor analysis and principal component analysis are applied to understand the latent structure of the data sets, and we have identified a total of four factors in pre-monsoon, three factors in monsoon and three factors in post-monsoon season, which are responsible for the whole data structure. These factors have explained that 90.908, 89.078 and 85.456 % of the cumulative percentage variance of the pre-monsoon, monsoon and post-monsoon data sets. Overall analysis reveals that the agricultural runoff, waste disposal, leaching and irrigation with wastewater, land transformation in the surrounding areas are the main causes of lake water pollution followed by some degree of pollution from geogenic sources such as rock weathering. Hence, there is an urgent need of proper attention and management of resources.

Multi-environment trial data are required to obtain stability performance parameters as selection tools for effective cultivar evaluation. The interrelationship among several stability parameters and their associations with mean yield, along with the repeatability of these parameters in consecutive years was the objective of this study. Barley yield data of 18 cultivars, proprietary of Dryland Agricultural Research Institute, evaluated in 12 locations over 2005-2007 in three locations of Iran was used for the combined analysis of variance in three datasets. I: Across locations in a single evaluation year (dataset A), II: Across locations in each of two single evaluation year (dataset B), III: Across all locations in three years (dataset C). For each dataset, cultivar phenotypic variance and other statistics were appropriately partitioned in its components. The interrelationship among the parameters and their associations with mean yield based on Spearman rank correlation was studied in each of the three single evaluation years (dataset A). Rank correlation coefficients were also used as estimates of the repeatability of these stability parameters across two year combinations (dataset B). The parameters 2  si Shukla and 2 Wi were consistently highly correlated with each other but not with mean yield in all single and pair evaluation years. The parameters bi, 2 Si and CV were consistently highly correlated with each other but not with mean yield in all single and pair evaluation years. Result showed that Shukla variance, Wricke ecovalence, SIPC1 and ASV AMMI stability statistics have high repeatability. Thus it is suggested that more stability statistics especially nonparametric methods be used for determination of repeatability methods.

The relative impacts of different types of land use on the surface water quality are yet to be ascertained and quantified. In this paper, the influence of different types of land use on surface water quality is investigated. Rain events samples from different land use in the central plateau, Iran, were analyzed for major ions. Statistical analyses were employed to examine the statistical relationships of land use and water quality on a regional scale in Iran central plateau. Principal component analysis was used to investigate the processes controlling the effects of land use on the water quality in this area. The higher correlations of range than other land uses with major ion, specifically pH and HCO3, were showed and it's maybe reflecting the effects of the season the samples were taken.

The main focus of present study was to demonstrate the possible generation sources of metal contamination in soil. The only authorized waste disposal site at Rajbandh located at Khulna in Bangladesh. To this endeavors, total sixty soil samples were collected at a depth of 0-30 cm from the existing ground surface and the relevant metal elements of Al, As, Ba, Ca, Cd, Cr, Co, Cu, Fe, Hg, K, Mn, Na, Ni, Pb, Sb, Sc, Sr, Ti, V and Zn were measured through standard methods in laboratory. Desired results for normality test obtained from normal QQ plot using XLSTAT. Almost all the metal elements were normally distributed in both the seasons. Multivariate statistics such as Pearson’ s correlation, principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) were performed using XLSTAT to show the correlation between metal elements and their possible generation sources. Results of Multivariate statistics revealed that almost all the metal elements were strongly correlated indicating same generation sources. In addition, results of PCA and AHC depicted that almost all the metal elements in soil derived from anthropogenic/human activities; least number of metal elements from natural sources as well as from both the natural and anthropogenic sources. Proper identification and control of possible generation sources of metal elements may reduce the threat of soil contamination due to metal elements in waste disposal site.