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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|贝叶斯分析代写Bayesian Analysis代考|Are You More Likely to Die in an Automobile Crash When the Weather Is Good Compared to Bad?

We saw in Chapter 2, Section $2.3$ some data on fatal automobile accidents. The fewest fatal crashes occur when the weather is at its worst and the highways are at their most dangerous. Using the data alone and applying the standard statistical regression techniques to that data we ended up with the simple regression model shown in Figure 3.1.

But there is a grave danger of confusing prediction with risk assessment. For risk assessment and management the regression model is useless, because it provides no explanatory power at all. In fact, from a risk perspective this model would provide irrational, and potentially dangerous, information. It would suggest that if you want to minimize your chances of dying in an automobile crash you should do your driving when the highways are at their most dangerous – in the winter months.

Instead of using just the total number of fatal crashes to determine when it is most risky to drive, it is better to factor in the number of miles traveled so that we can compute the crash rate instead, which is defined as the number of fatal crashes divided by miles traveled. Fortunately we have ready access to this data for the northeastern states of the United States in 2008 on a monthly basis as shown in Table 3.1.

As explained in the sidebar, the crash rate seems to be a more sensible way of estimating when it is most risky to drive.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|When Ideology and Causation Collide

One of the first things taught in an introductory statistics course is that correlation is not causation. As we have seen, a significant correlation between two factors $\mathrm{A}$ and $\mathrm{B}$ (where, for example $\mathrm{A}$ is yellow teeth and $\mathrm{B}$ is cancer) could be due to pure coincidence or to a causal mechanism such that:
a. A causes $\mathrm{B}$
b. B causes A
c. Both $\mathrm{A}$ and $\mathrm{B}$ are caused by $\mathrm{C}$ (where in our example $\mathrm{C}$ might be smoking) or some other set of factors.
The difference between these possible mechanisms is crucial in interpreting the data, assessing the risks to the individual and society, and setting policy based on the analysis of these risks. However, in practice causal interpretation can collide with our personal view of the world and the prevailing ideology of the organization and social group, of which we will be a part. Explanations consistent with the ideological viewpoint of the group may be deemed more worthy and valid than others, irrespective of the evidence. Discriminating between possible causal mechanisms $\mathrm{A}, \mathrm{B}$, and $\mathrm{C}$ can only formally be done if we can intervene to test the effects of our actions (normally by experimentation). But we can apply commonsense tests of causal interaction to, at least, reveal alternative explanations for correlations.
Box $3.1$ provides an example of these issues at play in the area of social policy, specifically regarding the provision of prenatal care.

# 贝叶斯分析代考

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|When Ideology and Causation Collide

：一个原因乙

c。两个都一个和乙是由C（在我们的例子中C可能是吸烟）或其他一些因素。

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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。

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