# Prediction the biodegradation rate of soil contaminated with different oil concentrations

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## Keywords:

biodegradation, oil, pollution, regression, soil, visual and mathematical analysis## Abstract

A multiple linear regression model is a practical statistical model for estimating relationships between a continuous dependent variable and predictor variables. The model itself is linear in that it consists of additive terms, each representing a predictor multiplied by an estimate of the coefficient. In addition, a constant (free term) is usually added to the model as well. Using two bacterial species (wild and recombinant), this paper develops a multiple model linear regression model to predict the biodegradation rate of soil contaminated with different oil concentrations. Factors such as crude oil concentration, number of days of incubation, and type of microbial strain were discovered to significantly influence the biodegradation rate based on visual and mathematical analysis. Mathematical models were developed to predict the biodegradation rate. The equation developed using multiple linear regression predicted the biodegradation rate with a coefficient of determination R2=0.549. The equation developed using polynomial regression predicted the biodegradation rate with a coefficient of determination R2=0.799. The resulting equations can be used to understand the relationship between the variables and also to predict the biodegradation rate of petroleum products.

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