Elisha Johnston
This paper introduces polynomial regression, which is useful for analyzing curvilinear data. I illustrate the power of the procedure with biological data I generated for the 2019 science fair. To select the best fitting model, I employed Bayesian Information Criterion. BIC reduces overfitting by penalizing higher order models. To visualize model fit, I graphed the selected model function in a data graph. To estimate the best fitting model with coefficient variance maximally accurate, I centered the data and then confirmed with orthogonal polynomial regression. In reducing multi-collinearity, I surfaced that some coefficients were not statistically significant, but retained in the model to meet model hierarchy guidelines. This paper is geared to a general audience.
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