@article{Adesanya_Taiwo_Adedodun_Olatayo_2018, title={Modeling Continuous Non-Linear Data with Lagged Fractional Polynomial Regression}, volume={6}, url={https://ajouronline.com/index.php/AJAS/article/view/5492}, DOI={10.24203/ajas.v6i5.5492}, abstractNote={<p>Fractional Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable is modelled as a 1/nth degree polynomial. Thus, this work is used to propose an extension of Fractional Polynomial Regression (FPR) term Lagged Fractional Polynomial Regression (LFPR) which is an alternative method to traditional techniques of analysing the pattern and degree of relationship between two or more continuous non-linear data. The coefficients of the proposed method were estimate using Maximum Likelihood Estimation method. From the results, the LFPR model indicated that for a unit increase in Evaporation, Humidity and Temperature there will be an increase in the millimeter of rainfall series on yearly basis. The value of coefficient of variation (R<sup>2</sup>) for the LFPR and FPR were 99% and 77%. While the value of adjusted Coefficient of Variation (R<sup>2</sup>) for LFPR and FPR were 96% and 75% respectively. Hence, the proposed method outperformed and adequately explained the variation in the dependent variable better than Fractional Polynomial Regression based on the values (R<sup>2</sup>) and adjusted (R<sup>2</sup>).</p>}, number={5}, journal={Asian Journal of Applied Sciences}, author={Adesanya, Kazeem Kehinde and Taiwo, Abass Ishola and Adedodun, Adebayo Funmi and Olatayo, Timothy Olabisi}, year={2018}, month={Oct.} }