### 统计代写|决策与风险作业代写decision and risk代考|Methods

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• 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

## 统计代写|决策与风险作业代写decision and risk代考|FMEA

The FMEA is commonly implemented in the risk assessment process as a powerful method for risk assessment and reliability analysis that is developed for the aerospace industry in the 1960 s at Grumman Aircraft Corporation (Bowles and Peláez 1995; Stamatis 2003). The preidentified failure modes’ risk priority orders are determined by the RPN approach (Liu et al. 2019; Chin et al. 2009). A ten point-scale is used for each parameter in FMEA. Limitations to the conventional FMEA method lead to the emergence of some new FMEA-based approaches. While in some studies, MCDM is merged with FMEA, artificial intelligence, inference systems, soft computing, and some miscellaneous tools are also integrated with FMEA (Chai et al. 2016).

## 统计代写|决策与风险作业代写decision and risk代考|BWM

BWM was proposed by Rezaei (2015) to solve MCDM problems. BWM is a pairwise comparison-based weighting method. The proposed method is beneficial in a way. ( $i$ ) Decision-makers determine the best and worst criteria among all criteria, pairwise comparison of best criterion with other criteria and worst criterion with others. There is no need for a pairwise comparison for all criteria. (ii) Some of the pairwise comparison-based MCDM methods use single vectors. (e.g., Swing and SMART family) Although these methods are data and time-efficient, they do not allow consistency checks. Some pairwise comparison-based methods (e.g., AHP) require a full pairwise comparison matrix. These methods allow consistency check, but they are not data and time-efficient. BWM requires less pairwise comparison compared to methods. It also allows for consistency check by having best to others and other to worst vectors. BWM is superior to other MCDMs in these aspects (Rezaei et al. 2016; Rezaei 2020).

Step 1. The criteria to be evaluated are determined. The criteria to be used in decision making are shown with $\left(c_{1}, c_{2} \ldots, c_{n}\right)$.

Step 2. Best (most significant, most desired) and worst (least significant, least desired) criteria are determined among the determined criteria. Pairwise comparison is not performed at this stage.

Step 3. Using the numbers 1-9, it is determined how the best criterion differs from other criteria. The Best to other vector is created as:
$$A_{B}=\left(a_{B 1}, a_{B 2}, \ldots, a_{B n}\right)$$
where $a_{B j}$ shows the predilection of the best criterion $B$ over criterion $j$ Comparison of the criteria with themselves $\left(a_{B B}=1\right)$

Step 4. Using the numbers 1-9, it is determined how the worst criterion differs from other criteria. Others-to-Worst vector is created as:
$$A_{B}=\left(a_{1 W}, a_{2 W}, \ldots, a_{n W}\right)$$
where $a_{j w}$ shows the predilection of the criterion $j$ over the worst criterion $W$.
Step 5. Determination of weight (( $\left.W_{1}^{}, W_{2}^{}, \ldots W_{n}^{*}\right)$.
The optimum weight for the criteria is the one where for each pair of $w_{B} / w_{j}$ and $w_{j} / w_{w}$ we have $w_{B} / w_{j}=a_{j w}$. To satisfy these for all $j$, we should find a solution where the maximum absolute differences $\left|\frac{w_{p}}{w_{j}}-a_{B j}\right|$ and $\left|\frac{w_{j}}{w_{w}}-a_{j w}\right|$ for all $j$ is minimized. Given that the variables cannot be negative, and the sum of the variables is equal to one, the problem to be solved is:

$$\min {j}\left{\left|\frac{w{B}}{w_{j}}-a_{B j}\right|,\left|\frac{w_{j}}{w_{W}}-a_{j w}\right|\right}$$
S.t
$$\sum w_{j}=1$$
$w_{j} \geq 0$ for all $j$.
With the necessary conversion done, the problem is:
$\min \xi$
\mid \begin{aligned}&\frac{w_{\beta}}{w_{j}}-a_{B j} \mid \leq \xi \text { for all } j . \&\frac{w_{j}}{w w}-a_{j w} \mid \leq \xi \text { for all } j\end{aligned}
$$\sum w_{j}=1$$
$w_{j} \geq 0$, for all $j$.
Solving problem, the optimum weights $\left(\left(W_{1}^{}, W_{2}^{}, \ldots W_{n}^{}\right)\right.$ and $\xi^{}$ are calculated. Following the procedure in Rezaei (2015), the Consistency Ratio (CR) is calculated. The higher the $\xi^{*}$, higher CR and less reliable results will be obtained.

As a result of the solution of the problem, the variable weights $\left(\left(W_{1}^{}, W_{2}^{}, \ldots W_{n}^{}\right)\right.$ ind $\xi^{}$ are calculated. Then the consistency ratio is calculated. When the number of rariables exceeds three, CR can never be equal to zero. It can be said that the lower he CR, the more consistent the evaluation is made.

## 统计代写|决策与风险作业代写decision and risk代考|Proposed Framework

The proposed FMEA framework is based on BWM method. The initial steps are about preparation for FMEA (determine failure modes and define RPN elements). The failure modes are identified that cause faulty products in the observed manufacturing plant. Then, the importance weights of the RPN elements and ranking of failure modes are calculated using BWM procedure. Preference values of each failure mode are computed with respect to $S, O$, and D. The flowchart of this proposed framework is provided in Fig. 3.2.

## 统计代写|决策与风险作业代写decision and risk代考|FMEA

FMEA 通常在风险评估过程中实施，作为一种强大的风险评估和可靠性分析方法，该方法是 1960 年代格鲁曼飞机公司为航空航天工业开发的（Bowles 和 Peláez 1995；Stamatis 2003）。预先确定的故障模式的风险优先顺序由 RPN 方法确定（Liu 等人 2019；Chin 等人 2009）。FMEA 中的每个参数使用十点量表。传统 FMEA 方法的局限性导致出现了一些基于 FMEA 的新方法。而在一些研究中，MCDM 与 FMEA 合并，人工智能、推理系统、软计算和一些杂项工具也与 FMEA 集成（Chai et al. 2016）。

## 统计代写|决策与风险作业代写decision and risk代考|BWM

BWM 由 Rezaei (2015) 提出来解决 MCDM 问题。BWM 是一种基于成对比较的加权方法。所提出的方法在某种程度上是有益的。(一世) 决策者确定所有标准中的最佳和最差标准，将最佳标准与其他标准以及最差标准与其他标准进行成对比较。不需要对所有标准进行成对比较。(ii) 一些基于成对比较的 MCDM 方法使用单个向量。（例如，Swing 和 SMART 系列）尽管这些方法具有数据效率和时间效率，但它们不允许进行一致性检查。一些基于成对比较的方法（例如，层次分析法）需要一个完整的成对比较矩阵。这些方法允许进行一致性检查，但它们不是数据和时间效率的。与方法相比，BWM 需要较少的成对比较。它还允许通过对他人最好和对最差向量进行一致性检查。BWM 在这些方面优于其他 MCDM（Rezaei 等人 2016；Rezaei 2020）。

## 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。