Abstract: Sustainable forestation, its management and practices seek the importance for an estimation tool. Hence, the aim of this study was to estimate the parameters of the dependent variable (stem volume) having a polynomial relationship with the independent variables using the Polynomial Regression (PR) technique. Field data collection involved measurements on 130 trees of an indigenous timber species, Cinnamomum iners. The stem height, diameter at base, middle and the top of the stem before the crown were tree measurements taken as variables. The stem volume was based on the Newton’s formula. Variables normality and linearity exhibited polynomial characterization of power terms greater than 2. Thirty-two Polynomial Regression Models (PRM) with the auxiliary variables were considered up to their third order interactions. Preliminary, multicollinearity between the independent variables was minimized and statistical tests involving the Global, Coefficient and Wald tests were carried out to select significant variables with their possible interactions. Comparisons between the Polynomial Regression Models (PRM) were made using the eight selection criteria (8SC). The best regression model (P26.5.3) with five multicollinearity and three insignificant variables removed, was identified based on the minimum value on majority of the 8SC. The Goodness-of-fit tests were done to validate the chosen best model. The use of an appropriate transformation was found to have increase in the degree of a statistically valid polynomial, hence, providing an improved estimation for tree stem volume.