Currently, feature screening is becoming an important topic in the fields of machine learning and high-dimensional data analysis. Filtering out irrelevant features from a set of variables is considered to be an important preliminary step that should be performed before any data analysis. Many approaches have been proposed to the same topic after the work of Fan and Lv (J. Royal Stat. Soc., Ser.
This article presents results related to the direct solution of the polynomial regression parameters based on the analytical solving of regression equations. The analytical solution is based on the normalization of the values of independent quantity with equidistance steps. The proposed solution does not need to directly solve a system of polynomial regression equations. The direct expressions to calculate estimators of regression coefficients, their standard deviations, and also standard and expanded deviation of polynomial functions are given.