ols造句

• Properties of ols : minimize the sum of squared residuals
  Ols性质：最小化残差平方和。
• We are discussing whether ols estimator satisfy asymptotic normality
  我们讨论是否ols估计量满足渐近正态性。
• The error cannot be observed but can be estimated from ols residuals
  不可观测的误差可以通过ols残差进行估计。
• Without this assumption , ols will be biased and inconsistent
  如果这个较弱的假定也不成立， ols将是有偏而且不一致的。
• Study of 2 - iodo - 3 - phenylsulfinyl - 2 - propen - 1 - ols by self - chemical ionization mass spectrometry
  醇类化合物自身化学电离质谱研究
• We begin by establishing the unbiasedness of ols estimators under a set of assumptions
  首先，我们在一些假定下证明ols估计量的无偏性。
• The urbanization model and process in bohai sea surrounding area in the 1990s by using dmsp ols data
  环渤海经济圈城市群能级梯度分布结构与区域经济发展战略研究
• We say that ols estimators are asymptotically efficient among a certain class of estimators under the gauss - markov assumptions
  我们说在高斯-马尔可夫假定下ols估计量是渐近有效的估计量。
• Exception : if the new regressor is perfectly multicollinear with the original regressors , then ols cannot be implemented
  例外：如果这个新解释变量与原有的解释变量完全共线，那么ols不能使用。
• A general proof of consistency of the ols estimators from the multivariate regression case can be shown through matrix manipulations
  多元回归中ols估计量的一致性的证明可以通过矩阵运算得到。
• It is worth emphasizing that a seemingly low r - squared does not necessarily mean that an ols regression equation is useless
  在社会科学中，特别是在截面数据分析中,回归方程得到低的r -平方值并不罕见。
• If ols chooses any value other than zero , it must be that this value reduced the ssr relative to the regression that excludes the regressor
  如果ols使此解释变量取任何非零系数，那么加入此变量之后， ssr降低了。
• Reason no . 1 : we may prefer to report the usual ols standard errors and test statistics unless there is evidence of heteroskedasticity
  理由1 ：除非有证据显示异方差存在，我们仍会偏好于常规ols的标准差及检验统计量。
• Idea : because the ols estimates are chosen to minimize the sum of squared residuals , the ssr always increases when variables are dropped from the model
  由于ols是用于最小化残差平方和,当有变量被从模型中舍弃时, ssr必定上升
• To prove that ols estimators are asymptotically efficient , one needs to ( 1 ) present an estimator that is consistent but its variance is larger
  为了证明ols估计量是渐近有效的，我们需要( 1 )给出一致的估计量但证明它有更大的方差。
• In such case , the usual ols standard error is invalid . the t statistics is also invalid , and tend to be too large in the case of > 0
  在这种情况下，通常的ols标准差不再正确。 t统计量也不再正确。在第二项为正的情况下， t会变大。
• The results are compared with the ols ' s results ( supposing the two transition be the second order transition ) and all results are plotted in the same figure
  作为比较我们把slo结果(假定两次转变都为二级转变)和我们的结果画在同一图上。
• If ols happens to choose the coefficient on the new regressor to be exactly zero , then ssr will be the same whether or not the second variable is included in the regression
  如果ols恰好使第二个解释变量系数取零，那么不管回归是否加入此解释变量， ssr相同。
• If we are presented with an estimator that is both linear and unbiased , then we know that the variance of this estimator is at least as large as that from ols
  如果有人向我们提出一个线性无偏估计量，那我们就知道，此估计量的方差至少和ols估计量的方差一样大。