The next step in the simple linear regression procedure is to determine if the remaining squared error of prediction is acceptable or not.
这个简单线性回归过程中的下一步是确定其余的预测方差是否可以接受。
Using a least-squared-error criterion to determine the line of best fit involves finding estimates of m and b that minimize the squared error of prediction.
使用最小方差法来确定最吻合的直线涉及寻找使预测方差最小的m和b的估计值。
Through comparing their sums of squared error, it was concluded that prediction error algorithm-based OE model has the best precision.
通过误差平方和的比较，确定利用基于输出误差（OE）模型的预报误差法所建立的模型的精度最高。
The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model.
最终预测结果为两个模型预测值之和， 以模型的预测标准偏差（RMSEP）作为评价指标， 以便考察新方法的有效性。
The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model.
最终预测结果为两个模型预测值之和， 以模型的预测标准偏差（RMSEP）作为评价指标， 以便考察新方法的有效性。
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