Least square means的意思

它是怎么得出的？ 用什么公式 ?
Least square means的意思shi 最小二乘junzhi.jiu shi pingjun shu de yisi.
xuetongjidenawei,neng geichu"SAS"chengxuma?

wobuyaonihefangcheng,jiushizhegeshiyan,http://zhidao.baidu.com/question/75132373.html,zenmelaitongjifenxi?

Least squares
From Wikipedia, the free encyclopedia
Jump to: navigation, search
The method of least squares or ordinary least squares (OLS) is used to solve overdetermined systems. Least squares is often applied in statistical contexts, particularly regression analysis.

Least squares can be interpreted as a method of fitting data. The best fit in the least-squares sense is that instance of the model for which the sum of squared residuals has its least value, a residual being the difference between an observed value and the value given by the model. The method was first described by Carl Friedrich Gauss around 1794.[1] Least squares corresponds to the maximum likelihood criterion if the experimental errors have a normal distribution and can also be derived as a method of moments estimator. Regression analysis is available in most statistical software packages.

The discussion is presented in terms of polynomial functions but any function can be used in least-squares data fitting. For example, a Fourier series fit is optimal in the least-squares sense.

Least squares From Wikipedia, the free encyclopedia Jump to: navigation, search The method of least squares or ordinary least squares (OLS) is used to solve overdetermined systems. Least squares is often applied in statistical contexts, particularly regression analysis. Least squares can be interpreted as a method of fitting data. The best fit in the least-squares sense is that instance of the model for which the sum of squared residuals has its least value, a residual being the difference between an observed value and the value given by the model. The method was first described by Carl Friedrich Gauss around 1794.[1] Least squares corresponds to the maximum likelihood criterion if the experimental errors have a normal distribution and can also be derived as a method of moments estimator. Regression analysis is available in most statistical software packages. The discussion is presented in terms of polynomial functions but any function can be used in least-squares data fitting. For example, a Fourier series fit is optimal in the least-squares sense. 哈哈

least-square-means 最小二乘配点法 this paper analyses elastic shell-bending problems by means of the least-squarecollocation method.  本文用最小二乘配点法分析弹性壳体弯曲问题。 很高兴第一时间为您解答，祝学习进步 如有问题请及时追问，谢谢~~o(∩_∩)o

好象是最小平方数的意思， 比如：一个式子有X 20 次方 + Y 3 次方 + X 什么的，那么最小平方数就是1了，以为最后一个X的平方是1。

公式：k(斜率) = [y(均) * x(均) - (y * x)(均)] / [x(均)^2 - x^2(均)] b(截距) = y(均) - k * x(均) 其中： y(均)表示你实验所得的所有y值的平均数。 x(均)表示你实验所得的所有x值的平均数。 (x*y)(均)表示所有x,y对应项相乘再取平均数。 x^2(均)表示所有x平方以后取的平均数。 最后拟合的直线为 y = k * x + b 式中的x,y可表示成一次函数关系的所有量。

最小二乘法。 简单来说，一堆分散数据中可以找出一条拟合直线，用统计软件可求出这条直线方程，使每个原始数据到这条直线的距离平方和最小，即残差平方和最小，残差是指真实值与拟合值之差。这种方法就是最小二乘法。 本人学Statistics的。 抱歉~SAS程序设计还没学到，如果是要找出拟合方程，用EVIEWS软件就可以做到了，将数据输入后，从Quick-Estimate Equation，再依次输入因变量、常数、自变量（可能有多个），即可计算出回归统计指标，再从view-representions里可以得到拟合方程。