简单汉语中“least squares”的意思
least squares是一个英语单词，有几个不同的含义。 让我们用例句来解释每个的含义和用法！
Uses an equation to calculate the
least squares
fit through points, for example, y = ax^b.
使用公式计算多个点的
最小二乘
值。示例 y = ax^b。
The MultiPak, data reduction software offers a variety of tools for data reduction, including linear
least squares
fitting and curve fitting.
此外，使用以易用性着称的PHI MultiPak软件，可以轻松进行LLS（峰形
最小二乘
拟合）和曲线拟合等数据分析，并能快速解决问题。
The results of our model were compared with those obtained by the
least squares
method using four kinds of model functions.
利用该模型，我们使用四种模型函数进行了数值实验，并将结果与使用
最小二乘
法时的结果进行了比较，我们发现最小二乘法在学习区间内往往具有更高的精度，但该模型发现准确率更高。
This type of trendline uses the following equation to calculate the
least squares
fit through points
此趋势线类型使用以下公式计算多个元素的
最小二乘法
。
Under general conditions, the
least squares
estimator of θ is asymptotically unbiased with asymptotic covariance matrix as follows
在正常情况下，θ 的
最小二乘
估计量是渐近无偏的，并且具有渐近方差-协方差矩阵
Unlike PLS, however, GSCA offers a global
least squares
optimization criterion, which is consistently minimized to obtain the estimates of model parameters.
然而，与 PLS 不同的是，GSCA 提供了全局
最小二乘
优化标准。不断地最小化以获得模型参数的估计。
The
least squares
means might not be estimable, and if not, they are marked nonestimable.
最小二乘均值
可能无法估计，在这种情况下，它被标记为不可估计。
Next, we give a sufficient condition concerning B for the proposed methods to give a
least squares
solution without breakdown for arbitrary right hand side b, for over-determined, under-determined and possibly rank-deficient problems.Then, as an example for B, we propose the IMGS(l) method, which is an incomplete QR decomposition.
接下来，对于包括 m≥n（超定）、m<n（欠定）和秩损失在内的一般情况，我们证明这些方法可以解决任意右侧项 b
的最小二乘问题
而不会崩溃。我们推导出充分条件为矩阵B提供一个解，然后，作为B的一个例子，我们提出了IMGS(l)方法，这是一种不完全的QR分解。
The standard
least squares
fitting method translates this specification into a linear model as follows: The nominal variables define a sequence of indicator variables, which assume only the values 1, 0, and -1.
标准
最小二乘
模型拟合将该药物列转换为值为 1、0、-1 的虚拟变量并拟合线性模型。
Where the constant of the model is not set to a given value, the explanatory power is evaluated by comparing the fit (as regards
least squares
) of the final model with the fit of the rudimentary model including only a constant equal to the mean of the dependent variable.
方差分析表：用于评估解释变量的解释力，如果模型的常数不设置为任意值，则最终模型的拟合优度（以
最小二乘法
表示）为 解释力为通过比较仅包含等于均值的常量的基本模型的拟合优度来评估。如果设置了模型常量，则针对因变量等于设置常量的模型进行比较。模型参数：显示参数估计值、相应的标准误差、Student’s t、相应的概率和置信区间。模型公式：为了使模型更易于破译和重用，显示模型方程。
In this paper, we consider alternative methods using an n ¡ßm matrix B to transform the problem to equivalent
least squares
problems with square coefficient matrices AB or BA, and then applying the Generalized Minimal Residual (GMRES) method, which is a robust Krylov subspace iterative method for solving systems of linear equations with nonsymmetric coefficient matrices.
本文首先作为替代方案，将原来的最小二乘问题转化为使用一个n×m矩阵B，以方阵AB或BA为系数矩阵的等价
最小二乘
问题，非对称方阵为我们提出了一种应用广义最小残差法（GMRES）的方法，这是一种用于线性方程组的鲁棒 Krylov 子空间迭代方法。
The SOR inner-iteration left/right-preconditioned generalized minimal residual (BA/AB-GMRES) methods determine a
least squares
solution/the minimum-norm solution of linear systems of equations without breakdown even in the rank-deficient case.
使用SOR内部迭代进行左（右）预处理的广义最小残差法（BA（AB）-GMRES）方法可以求解
最小二乘解
（线性方程给出最小范数解）。
As the parameter value is moved up, all the other parameters are adjusted to be
least squares
estimates subject to the change in the profiled parameter.
然而，当更改适当的参数以实现目标 SSE 时，将调整其他参数以实现
最小二乘
估计。
To experiment with this example, you can change such parameters as the channel impulse response, the number of equalizer tap weights, the recursive
least squares
(RLS) forgetting factor, the least mean square (LMS) step size, the MLSE traceback length, the error in estimated channel length, and the maximum number of errors collected at each Eb/No value.
要运行此示例，您需要通道脉冲响应、均衡器抽头权重的数量、递归
最小二乘
(RLS) 遗忘因子、最小均方 (LMS) 步长、MLSE 回溯长度和估计通道长度。您可以更改参数，例如为每个 Eb/No 值计算的错误和最大错误数。
Rows of Y correspond to observations and columns correspond to variables.Probabilistic principal component analysis might be preferable to other algorithms that handle missing data, such as the alternating
least squares
algorithm when any data vector has one or more missing values.
Y 的行对应于观测值，列对应于变量。如果您的数据向量具有一个或多个缺失值，则随机主成分分析可能优于其他可识别缺失值的算法（例如交替
最小二乘
算法）。
Using endogenous variable is in contradiction with the linear regression assumptions. This kind of variable can be encountered when variable are measured with error.The general principle of the two-stage
least squares
approach is to use instrumental variables uncorrelated with the error term to estimate the model parameters.
内生变量的使用与线性回归的假设不一致，这类变量是在对变量进行误差测量时发现的，逐步
最小二乘法
的一般原理是为了达到这个目的，我们使用与线性回归不相关的工具变量误差项。这些工具变量与内生变量相关，但与模型的误差项无关。
However, the tests and details in the
Least Squares
means and Parameter Estimates tables for them show correspondingly different highlights.
但是，当您查看
最小二乘
均值和参数估计表中显示的测试和详细信息时，您可以看到每种方法侧重于不同的领域。
When you specify only fixed effects for a Standard
Least Squares
fit, the Fit Model launch window appears as shown in Figure 2.5.
仅使用[标准
最小二乘法
]指定固定效应时的模型拟合启动窗口如图2.5所示。
The
least squares
calibration results for the workpiece appear.
显示工件
最小二乘
校准的结果。
Calculates the
least squares
fit through points by using the following equation
使用以下公式计算适用于多个元素的
R 平方值
。
听听“ least squares ”地道发音(发音)！
读法是【liːst*】。 听下面的视频并大声发音【liːst*】。