assignmentutor-lab™ 为您的留学生涯保驾护航 在代写数据可视化Data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化Data visualization代写方面经验极为丰富，各种代写数据可视化Data visualization相关的作业也就用不着说。

• 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|数据可视化代写Data visualization代考|The Phillips Curve

In economics, many people had studied changes in inflation, unemployment, import prices, and other variables over time, but it remained most common to plot these as separate series, as Playfair had done (Plate 10). The idea to plot one variable against another did not generally arise in scientific work.
In 1958 New Zealand economist Alban William Phillips published a paper in which he plotted wage inflation directly against the rate of unemployment in the United Kingdom from 1861 to 1957. Phillips discovered that, although both variables showed cyclic trends over time, they had a consistent inverse relation. His smoothed curve, ${ }^{29}$ shown in Figure 6.19, became one of the most famous curves in economic theory. It became important because economists could understand the co-variation in the two variables as representing structural constraints in an economy as a trade-off: to achieve reduced unemployment, the economy must suffer increased inflation (for example, by paying higher wages); to reduce inflation, it must allow more unemployment. With this understanding, policy makers could consider the desired balance between the two.

Figure $6.20$ is one of eleven other scatterplots presented in Phillips’s paper ${ }^{30}$ to illustrate the cyclic nature of inflation and unemployment. This graph also shows why a scatterplot is effective here and time-series plots would not be: the scatterplot shows the inverse relation directly, but the comparison of trends over time, as in Playfair’s chart of wages and prices (Plate 10), is at best indirect and is subject to the difficulties of using two different vertical scales.
Phillips was not the first economist to use scatterplots, even for time-based data, nor the first to have graphically derived curves named after him. ${ }^{31}$ Regardless of priority, Phillips’s hand-drawn overall scatterplot (Figure 6.19), combined with his careful parsing of the fitted curve into component cycles (Figure 6.20), provides a final example of Tukey’s dictum, another goal scored with a scatterplot.

## 统计代写|数据可视化代写Data visualization代考|Spurious Correlations and Causation

As the idea of the scatterplot developed, so too did the mistaken idea that you could plot any variable $y$ against another variable $x$, and “bingo!” the relationship thus revealed could be interpreted causally. Even though the fallacy, post hoc ergo propter hoc, has long been recognized as nonsense, sometimes a causal link seems strengthened with data in a scatterplot.

A 2012 illustration of this was both humorous and subtle. ${ }^{32}$ In an article published in the prestigious New England Journal of Medicine, Dr. Franz Messerli wondered, ${ }^{33}$ “Chocolate consumption could hypothetically improve cognitive function not only in individuals but in whole populations. Could there be a correlation between a country’s level of chocolate consumption and its total number of Nobel laureates per capita?” (Messerli, 2012, p. 1562 ).

The data from twenty-three countries are shown in Figure 6.21. The correlation shown in this plot is $r=0.79-$ not perfect, but suggesting a very strong relationship, which Messerli attributed to the high level of flavanols in chocolate. A popular article in Reuters used the headline, “Eat More Chocolate, Win the Nobel Prize. ${ }^{n 34}$ According to the data, if the average citizen ate only one more kilogram of chocolate per year, their country would gain another $2.5$ Nohel prizes.

A rebuttal and an answer of sorts was quickly provided by Pierre Maurage and others in the Journal of Nutrition (2013). To test other possible explanations for the startling influence of chocolate on Nobel prizes by country, they gathered more data, some of which is shown in Figure $6.22$.

# 数据可视化代考

## 统计代写|数据可视化代写数据可视化代考|虚假的相关性和因果关系

.

2012年的一个例子既幽默又微妙。${ }^{32}$在著名的《新英格兰医学杂志》上发表的一篇文章中，弗朗茨·梅塞利博士想知道，${ }^{33}$“吃巧克力可以改善认知功能，假设不仅是个人，而且是整个人群。一个国家的巧克力消费水平与该国的人均诺贝尔奖得主总数之间是否存在相关性?”(Messerli, 2012, p. 1562)。

Pierre Maurage等人很快在《营养学杂志》(2013)上给出了反驳和各种各样的答案。为了检验巧克力对各个国家诺贝尔奖得主惊人影响的其他可能解释，他们收集了更多的数据，其中一些数据如图$6.22$所示

## 有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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
assignmentutor™您的专属作业导师