Comparing Discriminant Analysis and Logistic Regression Model as a Statistical Assessment Tools of Arsenic and Heavy Metal Contents in Cockles

Abstract

Two statistical techniques; discriminant analysis (DA) and logistic regression model were used to analyze the concentration of arsenic and heavy metal contents in cockles (Anadara granosa) from two estuaries in the state of Penang, Malaysia.  This study was undertaken to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status. Arsenic (As), chromium (cr), cadmium (cd), zinc (zn), copper (cu) and lead (pb) were analyzed using a graphite flame atomic absorption spectrophotometer (GF-AAS) whilst mercury (Hg) was analyzed using a cold vapor atomic absorption spectrophotometer (CV-AAS). Logistic regression model showed that only two explanatory variables Zn (p < 0.01) and Cd (p