Open AccessPredict the amount of Cu using the four Ca, Al, P, S Elements by Multiple Linear Regression Method
Author(s) -
Hamed Nazerian
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38121
Subject(s) - bayesian multivariate linear regression , multivariate statistics , linear regression , statistics , regression , regression analysis , estimation , mathematics , copper , proper linear model , general linear model , segmented regression , metallurgy , engineering , materials science , systems engineering
The study area is located in Sarbisheh city in South Khorasan province, Iran. Copper estimation was performed by multivariate linear regression method to facilitate the use of previous analyses to predict this element in other areas, reduce costs and also reduce the number of samples. For this purpose, by obtaining a basic formula from estimating the amount of Cu with one of the promising points samples, the amount of copper in other parts of the exploration area was investigated. Several analyses were taken from the exploratory area after calculations to validate the regression. The regression results of new and old data were compared and estimation acceptable. These calculations were performed by SPSS software, according to the four elements Ca, Al, P, S, the results obtained and the relationship presented has acceptable validity. Keywords: Multivariate linear regression, Cu estimation, SPSS, Iran.