(E)-1-Isopropoxy-buta-1,3-dien | 32854-12-9

• 分子结构分类: 有机化合物 - 有机氧化合物
• 英文名称: (E)-1-Isopropoxy-buta-1,3-dien
• 英文别名: 1t-isopropoxy-buta-1,3-diene；buta-1,3-dien-tyl-isopropyl ether；buta-1,3-dienyl-isopropyl ether；1t-Isopropoxy-buta-1,3-dien；Buta-1,3-dien-tyl-isopropyl-aether；Buta-1,3-dienyl-isopropyl-aether；1-Isopropyloxy-butadien-(1.3)；(1E)-1-propan-2-yloxybuta-1,3-diene
• CAS: 32854-12-9
• 化学式: C₇H₁₂O
• 分子量: 112.172
• InChiKey: DYOAXOSGQFYNPH-AATRIKPKSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  2.3
• 重原子数：
  8
• 可旋转键数：
  3
• 环数：
  0.0
• sp3杂化的碳原子比例：
  0.43
• 拓扑面积：
  9.2
• 氢给体数：
  0
• 氢受体数：
  1

反应信息

• 作为产物：
  描述：

  trans-crotonaldehyde diisopropylacetal 生成 (E)-1-Isopropoxy-buta-1,3-dien
  参考文献：
  名称：

  US2573678

  摘要：

  公开号：

文献信息

• The Effect on Attribute Prediction of Location Uncertainty in Spatial Data
  作者：John Gabrosek、Noel Cressie

  DOI：10.1111/j.1538-4632.2002.tb01088.x

  日期：2002.7

  A datum is considered spatial if it contains location information. Typically, there is also attribute information, whose distribution depends on its location. Thus, error in location information can lead to error in attribute information, which is reflected ultimately in the inference drawn from the data. We propose a statistical model for incorporating location error into spatial data analysis. We investigate the effect of location error on the spatial lag, the covariance function, and optimal spatial linear prediction (that is, kriging). We show that the form of kriging after adjusting for location error is the same as that of kriging without adjusting for location error. However, location error changes entries in the matrix of explanatory variables, the matrix of co‐variances between the sample sites, and the vector of covariances between the sample sites and the prediction location. We investigate, through simulation, the effect that varying trend, measurement error, location error, range of spatial dependence, sample size, and prediction location have on kriging after and without adjusting for location error. When the location error is large, kriging after adjusting for location error performs markedly better than kriging without adjusting for location error, in terms of both the prediction bias and the mean squared prediction error.
• US2573678
  申请人：——

  公开号：——

  公开（公告）日：——