### 统计代写|决策与风险作业代写decision and risk代考|A Real-World Numerical Application

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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|Applies Bayesian BWM to Obtain Risk Factor Weights

As in the steps of Bayesian BWM introduced in Sect. 2.3.1, first, the seven risk analysts were invited to select the most and least important risk factors based on their judgments. By comparing the importance of the most important risk factors with other risk factors by the seven risk analysts, the BO vectors were constructed, as shown in Table $2.8$. For example, risk analyst 1 believed that $\mathrm{D}$ is the most important factor, and its importance compared to other factors is $2,4,1$, and 2 . Similarly, the OW vectors can be constructed by comparing other factors to the worst factor, as shown in Table 2.9. The Bayesian BWM questionnaires completed by all risk analysts have been checked for consistency to ensure the quality and logic of all questionnaires. Next, the MATLAB software provided by Mohammadi and Rezaei $(2020)$ was used to obtain the integrated weights of risk factors, as shown in Table $2.10$. The weights of the factors are $w_{\mathrm{S}}=0.2362, w_{\mathrm{O}}=0.2142, w_{\mathrm{D}}=0.3209$, and $w_{\mathrm{E}}=0.2287$, and their importance ranking is $\mathrm{D}>\mathrm{S}>\mathrm{E}>\mathrm{O}$.

## 统计代写|决策与风险作业代写decision and risk代考|Using Bayesian BWM to Evaluate the Risk Scores

In addition to measuring the importance of risk factors, Bayesian BWM also serves as a risk score assessment tool for failure modes. It uses each risk factor $\mathrm{S}, \mathrm{O}, \mathrm{D}$, and E as a basis to evaluate the relative importance of failure modes. For example, based on severity (S), FM9 is the most severe, and the severity scores compared to other failure modes are shown in Table 2.11. Next, FM6 is the least serious, and the scores of other failure modes compared to FM6 are shown in Table 2.12. According to this process, the failure modes can be evaluated under the 4 risk factors. The remaining survey data are shown in Tables $2.13,2.14,2.15,2.16,2.17$, and $2.18$.

In this step, the seven risk analysts evaluated each failure mode according to different risk factors, and the evaluation method used was based on pairwise comparisons. Bayesian BWM was used to integrate the evaluation data of all risk analysts and generate an initial evaluation matrix, as shown in Table 2.19. In Table 2.19, the sum of each column must be 1 , so there is no need for normalization.

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

The manufacturing process of machine tools is complicated and it is not easy to evaluate its reliability. It is feasible to diagnose potential critical failure modes of machine tools through FMEA. This study uses the classifiable TOPSIS technique to rank critical failure modes and classify them. For a more detailed introduction and concept of the classifiable TOPSIS technique, the study of Liaw et al. (2020) can be referred to. The weight results of the risk factors from Sect. 2.4.1 can be substituted into the calculation of classifiable TOPSIS, and the weighted normalized matrix can be obtained, as shown in Table $2.20$.

In Table $2.20$, PIS and NIS (representing maximization and minimization of risks) are:

$$\text { PIS }=(0.038,0.042,0.060,0.070) ;$$
$$\text { NIS }=(0.015,0.016,0.013,0.009) \text {. }$$
Next, the distance between the failure mode and PIS $\left(S^{+}\right)$and NIS $\left(S^{-}\right)$can be calculated through Eqs. (2.28) and (2.29). It is certain that the distance between the highest level and PIS must be 0, and similarly, the distance between the worst level and NIS is also 0. The distance between NIS and PIS is $0.084$. Table $2.21$ shows the analysis results of the classifiable TOPSIS. The top five failure modes in the ranking are improper waterproof measures (FM9), the positive/negative clearance of the inclined shaft exceeding the standard (FM5), oil leakage from the disk surface (FM8), the machine making noise when the inclined shaft rotates (FM6), and the inclined shaft reproducibility exceeding the standard (FM4). The closeness coefficient (CC ) of FM9 is $0.946$ as the maximum value, and it is at the highest risk level, indicating that it is a failure mode that needs to be solved and controlled urgently. In general, four failure modes fall into Risk Level B and three into Risk Level C. Therefore, decision-makers should devote all risk management resources to Levels $\mathrm{A}^{+}$and $\mathrm{B}$ to prevent these failure modes in order to reduce the risk of product failure.

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

开始 =(0.015,0.016,0.013,0.009).

## 广义线性模型代考

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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。