2022年7月4日

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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
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## 商科代写|商业数学代写business mathematics代考|Rank Order Centroid Method

This method is a simple way of giving weight to a number of items that are ranked according to their importance. The decision-makers usually can rank items much more easily than giving weight to them. This method takes those ranks as inputs and converts them to weights for each of the items. The conversion is based on the following formula:
$$w_{i}=\left(\frac{1}{M}\right) \sum_{n=i}^{M} \frac{1}{n}$$

1. List objectives in order from most important to least important
2. Use the aforementioned formulas for assigning weights
where $M$ is the number of items and $w_{i}$ is the weight of the $i$ item. For example, if there are four items, the item ranked first will be weighted $(1+1 / 2+1 / 3+1 / 4) / 4=0.52$, thesecond will be weighted $(1 / 2+1 / 3+1 / 4) / 4=0.27$, the third $(1 / 3+1 / 4) / 4=0.15$, and the last $(1 / 4) / 4=0.06$. As shown in this example, the rank order centroid (ROC) is simple and easy to follow, but it gives weights that are highly dispersed (Chang, 2004). As an example, consider the same factors to be weighted (shortening schedule, agency control over the project, project cost, and competition). If they are ranked based on their importance and influence on dêcision as 1-shoortening schëdule, 2-project cost, 3 -agency control over the project, and 4 -competition, their weights would be $0.52,0.27,0.15$, and $0.06$, respectively. These weights almost eliminate the effect of the fourth factor, that is, among competitors. This could be an issue.

In this method, the decision-maker should compare each item with the rest of the group and should give a preferential level to the item in each pairwise comparison (Chang, 2004). For example, if the item at hand is as important as the second one, the preferential level would be one. If it is much more important, its level would be 10 . After conducting all the comparisons and after determining the preferential levels, the numbers will be added up and normalized. The results are the weights for each item. Table $4.7$ can be used as a guide for giving a preferential level score to an item while comparing it with another one. The following example shows the application of the pairwise comparison procedure. Referring to the four critical factors identified earlier, let us assume that shortening the schedule, project cost, and agency control of the project are the most important parameters in the project delivery selection decision. Following the pairwise comparison, the decision-maker should pick one of these factors (e.g., shortening the schedule), compare it with the remaining factors, and should give a preferential level to it. For example, shortening the schedule is more important than project cost; in this case, it will be given a level of importance of $5 .$

The decision-maker should continue the pairwise comparison and should give weights to each factor. The weights, which are based on the preferential levels given in each pairwise comparison, should be consistent to the extent possible. The consistency is measured based on the matrix of preferential levels. The interested reader can find the methods and applications of consistency measurement in Temesi (2006). Table $4.7$ provides the nine-point scale that we will use.

Table $4.8$ shows the rest of the hypothetical weights and the normalizing process, the last step in the pairwise comparison approach.

Note that Column (5) is simply the sum of the values in Columns (1) through (4). In addition, note that if the preferential level of factor $i$ to factor $j$ is $n$, then the preferential level of factor $j$ to factor $i$ is simply $1 / n$. The weights calculated for this exercise are $0.6,0.1,0.2$, and $0.1$, which add up to $1.0$. Note that it is possible for two factors to have the same importance and weight.

## 商科代写|商业数学代写business mathematics代考|Rank Order Centroid Method

$$w_{i}=\left(\frac{1}{M}\right) \sum_{n=i}^{M} \frac{1}{n}$$

1. 按从最重要到最不重要的顺序列出目标
2. 使用上述公式分配权重
，其中 $M$ 是项目的数量和 $w_{i}$ 是重量 $i$ 物品。例如，如果有四个项目，则排名第一的 项目将被加权 $(1+1 / 2+1 / 3+1 / 4) / 4=0.52$ ，第二个将被加权
$(1 / 2+1 / 3+1 / 4) / 4=0.27$ ，第三 $(1 / 3+1 / 4) / 4=0.15$, 最后一个 $(1 / 4) / 4=0.06$. 如本例所示，排序质心 (ROC) 简单易懂，但它给出的权重高度分 散 (Chang, 2004)。例如，考虑要加权的相同因素（缩短工期、代理对项目的控制、 项目成本和竞争) 。如果根据它们的重要性和对决策的影响将它们排名为 1缩短计 划、2-项目成本、3-对项目的机构控制和 4-竞争，它们的权重将是
$0.52,0.27,0.15 ，$ 和 $0.06$ ，分别。这些权重几乎消除了第四个因素的影响，即竞 争对手之间的影响。这可能是一个问题。

## 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。