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相关概念视频

Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K
Design Example: Dimensioning of Concrete Masonry Construction01:13

Design Example: Dimensioning of Concrete Masonry Construction

264
For the construction of a storeroom using concrete masonry units, it's essential to align the dimensions of the structure with the actual sizes of the blocks and the intended mortar joints. On the site in question, there's a stockpile of concrete masonry blocks with a nominal size of eight by eight by sixteen inches, which are to be used in the construction of the storeroom.
The site engineer has laid out a plan for the storeroom with external dimensions of twelve feet in length and...
264
Method of Superposition01:20

Method of Superposition

1.7K
The method of superposition is a crucial technique in structural engineering, used to analyze the effect of multiple loads on beams. This approach involves calculating the deflection and slope for each load on a beam separately, and then summing these effects to determine the overall impact. It is applicable only when the beam material remains within its elastic limit, ensuring that deformations are linearly elastic.
When applying the method of superposition, each type of load—whether...
1.7K
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

221
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
221
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K

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相关实验视频

Updated: Jan 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

DSCostPred:用于建筑成本预测的双叠模型.

Chen-Ping Liu1, Xin-Gen Sun2, Jian-Hua Guan3

  • 1Department of Architectural Engineering, Hunan Defense Industry Polytechnic, Xiangtan, 411207, China.

Scientific reports
|December 20, 2025
PubMed
概括

由于复杂的变量相互作用,准确的建筑成本预测具有挑战性. 一种新的双堆叠方法 (DSCostPred) 通过对变量进行分类和使用组合模型来提高预测,其性能优于传统方法.

关键词:
建筑成本预测和预测双堆叠方法是双堆叠的方法.模型堆叠的模型变量堆叠可以变量堆叠.

相关实验视频

Last Updated: Jan 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

科学领域:

  • 工程 工程师 工程师 工程师
  • 建设管理建设管理.
  • 数据科学数据科学数据科学

背景情况:

  • 建筑成本预测至关重要,但由于多维,动态变量而复杂.
  • 不线性关系和变量之间的相互作用降低了预测的准确性.

研究的目的:

  • 开发一种先进的方法,准确地预测建筑项目的成本.
  • 为了应对复杂的变量相互作用和功能差异带来的挑战.

主要方法:

  • 提出了一种双叠建筑成本预测方法 (DSCostPred).
  • 实施可变堆叠用于预分类,以防止干扰.
  • 利用模型堆叠与多种算法来捕捉复杂的相互作用.
  • 集成变量和模型堆叠用于协作预测.

主要成果:

  • DSCostPred在真实世界的数据上表现出优越的性能,与经典方法相比.
  • 废弃实验证实了双堆叠方法的有效性.
  • SHAP分析验证了拟议方法的可行性和可解释性.

结论:

  • 双堆叠方法有效地处理建筑成本预测中的复杂变量关系.
  • DSCostPred为工程施工项目提供了强大而准确的解决方案.
  • 变量分类和集合建模是提高预测准确性的关键.