对应于方差参数这两种估计的回归系数的两种两步估计，它们的均方误差大致相当。
However, two-stage estimates of regression coefficients corresponding to these two estimates have approximate equal mean square error.
对回归方程误差的影响因素进行了分析，导出了回归方程的均方误差与其影响因素之间的关系式。
The influencing factors for errors in regression equation are discussed and the relations between mean square error of regression equation and its influence factors are deduced.
本文以多元线性回归模型的典则形式为研究对象，从减小均方误差的角度出发，在一定的范围内分析了岭参数K 的存在性和岭估计的优良性。
The investigation object of this paper is the standard canonical form. In order to the mean square error, I analyze the existence and choiceness of ridge regression K in some spectrum.
结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
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