Censored values of some elements like Au, As, Mo, S were replaced using Cohen Maximum Likelihood method. All data were inserted in Excel and SPSS and processed statistically. Statistical parameters of raw data, histograms and cumulative curves of elements were studied. Probable potential of each variable was determined based on raw data, for example high concentration of Au (14-167 mg/ton) in secondary halos was an evidence of the elementís hidden potential. Also, high concentration of Cu & Zn was found near an old mine in the study area. Outliers were detected and functions of the elements were normalized. Bivariate and multivariate studies were conducted on the functions of elements in such a way that correlation of geochemical data using Spearman and correlation of Au, Cu, Pb & Zn with other elements were taken into consideration. The results showed Au was available in the study area as an independent variable and had low correlation to other elements. Cu, Mn, Co, Mg, Al, Ni, Sc, Zn, Ti & Fe showed high correlation with each other. Cluster and factor analysis methods were used for better identification of genetic relationship, major elements were selected and factor map was drawn. Both methods showed the high correlation among Cu, Al, Mg, Mn, Zn, Co, Sc & Ni and also among V, Fe & Cr. In addition, Au & Mo showed high correlation. Data of mentioned elementsí anomalies were inserted in a table and shown in grid evaluation maps.
Univariate analysis was performed for heavy mineral samples and statistical parameters of studied minerals were inserted in a table. A map showing the distribution of important elements was prepared. The results showed the concentration of scheelite (12-35 particles) in western parts of the area. Also, ferrous minerals like magnetite, hematite, goethite & oligiste were associated with scheelite. Celestine was enriched in samples taken from western parts too.