In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

本文采用线性回归 (LR)、多元线性回归 (MLR) 和多项式回归 (PR) 技术来提出模型 Li-MuLi-Poly。该模型预测了在美利坚合众国发生的 COVID-19 死亡事件。在机器学习模型、最小均方误差模型和最大似然比模型上进行了实验。根据均方误差、调整均方误差、均方误差、均方根误差（RMSE）和最大似然比的度量选择最佳拟合模型，统计t-test 用于验证结果。数据集在应用于所提出的回归模型之前经过分析、清理和辩论。所选独立参数的相关性由热图和 Carl Pearson 相关矩阵确定。发现当所有独立参数都用于建模时，LR模型的准确性最适合数据集，但是与PR模型相比，RMSE和平均绝对误差（MAE）较高。当建模时没有考虑太多独立参数时，需要高度的 PR 模型来最佳拟合数据集。然而，当在建模中考虑所有维度的独立参数时，低度 PR 模型最适合数据集。