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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 商科代写|商业数学代写business mathematics代考|Strengths and Limitations to Data Envelopment Analysis

DEA can be a very useful tool when used wisely according to Trick (1996). A few of the strengths that make DEA extremely useful are (Trick, 1996): (1) DEA can handle multiple input and multiple output models, (2) DEA does not require an assumption of a functional form relating inputs to outputs, (3) DMUs are directly compared against a peer or combination of peers, and (4) Inputs and outputs can have very different units. For example, $X_{1}$ could be in units of lives saved and $X_{2}$ could be in units of dollars without requiring any a priori trade-off between the two.

The same characteristics that make DEA a powerful tool can also create limitations to the process and analysis. An analyst should keep these limitations in mind when choosing whether or not to use DEA. A few additional limitations include the following:

1. As DEA is an extreme point technique, noise in the data such as measurement error can cause significant problems.
2. DEA is good at estimating relative efficiency of a DMU, but it converges very slowly to absolute efficiency. In other words, it can tell you how well you are doing compared to your peers but not compared to a theoretical maximum.
3. As DEA is a nonparametric technique, statistical hypothesis tests are difficult and are the focus of ongoing research.
4. As a standard formulation of DEA with multiple inputs and outputs creates a separate linear program for each DMU, large problems can be computationally intensive.
5. Linear programming does not ensure that all weights are considered. We find that the values for weights are only for those that optimally determine an efficiency rating. If having all criteria weighted (inputs, outputs) is essential to the decision-maker, then do not use DEA.

## 商科代写|商业数学代写business mathematics代考|Modified Delphi Method

The Delphi method is a reliable way of obtaining the opinions of a group of experts on an issue by conducting several rounds of interrogative communications. This method was first developed in the U.S. Air Force in the 1950 s, mainly for market research and sales forecasting (Chan et al., 2001). This modified method is basically a way to obtain inputs from experts and then to average their scores.

The panel consists of a number of experts who are chosen based on their experience and knowledge. As mentioned earlier, panel members remain anonymous to each other throughout the procedure to avoid the negative impacts of criticism on the innovation and creativity of panel members. The Delphi method should be conducted by a director. One can use the Delphi method for giving weights to the short-listed critical factors. The panel members should give weights to each factor and their reasoning. In this way, other panel members can evaluate the weights based on the reasons given and can accept, modify, or reject those reasons and weights. For example, consider a search region that has rows A-G and columns 1-6 as shown in Figure 4.4. A group of experts then places an $x$ in the squares. In this example, each of 10 experts places $5 x^{\prime}$ s in the squares. We then total the number of $x^{\prime}$ s in the squares and divide by the total of $x$ placed, in this case $50 .$
We would find the weights as shown in Table $4.5$.

## 商科代写|商业数学代写business mathematics代考|Strengths and Limitations to Data Envelopment Analysis

1. 由于 DEA 是一种极值点技术，因此数据中的噪声（例如测量误差）可能会导致严重问题。
2. DEA 擅长估计 DMU 的相对效率，但它收敛到绝对效率非常缓慢。换句话说，它可以告诉您与同龄人相比您的表现如何，但不能与理论最大值相比。
3. 由于 DEA 是一种非参数技术，因此统计假设检验很困难，并且是正在进行的研究的重点。
4. 作为具有多个输入和输出的 DEA 的标准公式为每个 DMU 创建一个单独的线性程序，大型问题可能是计算密集型的。
5. 线性规划不能确保考虑所有权重。我们发现权重值仅适用于那些最佳地确定效率等级的值。如果对所有标准（输入、输出）进行加权对决策者来说是必不可少的，那么不要使用 DEA。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
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