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1)  least area method for linear regression
直线回归的最小面积法
2)  Line of least linear regression
最小一乘回归线
1.
Line of least linear regression through two Sample;
过两个样本点的最小一乘回归线
2.
Literature[1]says that two sample points(x_i,y_i)and(x_i,Y_i)can be surely found,the line through which is the line of least linear regression.
陈希孺在《最小一乘线性回归》一文中指出,必可找到两个样本点(x_i,y_i)和(x_j,y_j),过这两点的直线是一条最小一乘回归线。
3)  least square regression method
最小平方回归法
4)  least area method
最小面积法
1.
By the application of improved least area method, project management maturity is estimated in order to help enterprise to recognize own project management status and weak links.
基于Kerzner模型对一家建筑设计企业进行问卷调查,应用改进的最小面积法进行成熟度评价,帮助企业认清自身的项目管理现状及存在的薄弱环节。
2.
Linear regression equations obtained by least square method are different if independent variable x and dependent variable y are exdanged a least area method is proposed in this paper to solved the , calculating formulae are derived and properties are discussed for this method.
提出直线回归的最小面积法解决这一问题,推导了该方法的计算公式并讨论了该方法的一些性质。
5)  Rectification regression
直线化回归法
6)  Regression Line
回归直线
1.
In this paper, we gave the new regression line by l east distance approach.
本文中 ,我们应用最小距离的方法得到了新的回归直线方程 ,并与应用最小二乘方法所得的回归直线进行了比较 。
2.
The paper theoretically proves the relationship of two straight regression lines gained when diffeernt variables are selected in monadic linear regresson and further discusses the tangent of included angle of straight lines.
从理论上证明了一元线性回归中选取不同的变量时,所得两条回归直线的关系。
3.
In this paper, it is proved that two classical regression line with different independent variable are not overlap about same a set of sample.
本文证明了对于同一组样本数据 ,选取不同的变量为自变量时所得到的两条回归直线不重合 ,并且讨论了这两条不重合回归直线之间夹角与相关系数的关
补充资料:非线性最小二乘拟合
分子式:
CAS号:

性质:用最小二乘法拟合非线性方程。有些变量之间的非线性模型,通过变量变换可以化为线性模型,此称为外在线性。而有些变量之间的非线性模型,通过变量变换不能化为线性模型,通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε,其残差平方和。S(θ)是θ的函数,当模型关于θ是非线性的,正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值,拟合非线性回归方程。

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