A quadratic polynomial regression model was developed to assess the non-linear relationship between cumulative fluoroscopy dose and eye lens dose. The study involved the use of machine-generated ...

Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental ...

In this paper, we focus on the problem of polynomial regression with uncertain polynomial order and coefficients. For polynomial regression problem, a probabilistic graph model with polynomial ...

After dividing participants into 3 DHRPS groups (low, medium, high), a second‐order polynomial regression model estimated the effects of each DHRPS group on primary outcome associations across the age ...

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Estimated Marginal Means and Marginal Effects from Regression Models for ggplot2 - strengejacke/ggeffects ...

Guastello’s polynomial regression method for solving cusp catastrophe model has been widely applied to analyze nonlinear behavior outcomes. However, no statistical power analysis for this modeling ...

Polynomial Regression and Other Variants: Polynomial regression conveys the relationship between the dependent and independent variables as an nth degree polynomial—for example, ax².

The polynomial regression model consists of a local, weighted linear approximation of the outcome across levels of the running variable. This model estimates a local average treatment effect parameter ...