Published Paper Details:

Statistical regression is vital for thoroughly analyzing and optimizing Internal Combustion engine performance, serving various functions like providing predictive insights and aiding data-driven decision-making. In this study, two methodologies, no transform and square root transformation are compared for their effectiveness in predicting engine performance indicators brake thermal efficiency and specific fuel consumption in spark ignition engines fueled with pyro oil and gasoline blends. Analysis of Variance (ANOVA), the statistical analysis tool was used to find the significant of the model. The findings consistently favor the square root transformation, which demonstrates higher R-squared values and better alignment with actual values compared to no transform regression technique. Notably, it offers more accurate predictions of critical metrics such as brake thermal efficiency and specific fuel consumption compared to the traditional no transform approach. Overall, this research significantly contributes to refining engine performance prediction models, offering valuable insights for decision-making in automotive engineering and design.