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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|数据可视化代写Data visualization代考|Re-visioning the Broad Street Pump

Snow’s data and his map have become such classics in the lore of epidemiology and thematic cartography, that many people have attempted to reproduce or “improve” his map, in various ways, and for various purposes; these have not always been either historically accurate or with a positive effect. ${ }^{25}$ Two revisions of Snow’s map follow, both of which attempt answers to the question, “How could Snow have made his map more visually effective for his purpose?” but with different presentation goals and audiences in mind.

Figure $4.9$ is a very simplified (or dumbed-down) version in the style of a presentation graphic that Snow might have used in a PowerPoint presentation to the Board of Guardians in his petition to remove the pump handle (but we’re fairly certain Snow would have rejected this). It is actually two steps removed from Snow’s original. In a 1958 paper titled Pioneer Maps of Health and Disease in England, ${ }^{26}$ the Oxford social geographer Edmund William Gilbert drafted a slightly simpler version of Snow’s map, retaining only the major street names and replacing the black bars for deaths with dots. He also removed the labels of the anomalous cases of the workhouse and brewery that were crucial to Snow’s argument. Gilbert carelessly captioned his version, “Dr. John Snow’s map (1855) of deaths from cholera …” and misled later authors in thinking that this was indeed Snow’s map.

The re-vision in Figure $4.9$ was pared down from Gilbert even more by Mark Monmonier in How to Lie with Maps. He removed all place names, made the dots for deaths slightly smaller, and greatly magnified the circle symbols used for the pumps, adding a big arrow pointing to the one on Broad Street. About the only thing he didn’t do to the map was to use the title “BROAD STREET PUMP CAUSES CHOLERA” in large bold type. One could argue that this was aceeptable if the presentation goal was consent by the Board of Guardians to remove the pump handle. Lost in this translation, however, is Snow’s attempt to show visually the relation between cholera mortality and the sources of water.

## 统计代写|数据可视化代写Data visualization代考|Graphical Successes and Failures

As the compiler of abstracts, Farr was more inclined to use tables than graphs to present his reports to the home secretary and parliamentary committees. Yet the scale and importance of the cholera outbreaks over many years gave him so much data on so many variables that he was led to the use of charts to try to show patterns and seek relations with cholera mortality.

By this time, Playfair’s line graphs of time-series data (see Chapter 5) were relatively well-known and Farr’s use of this device in Figure $4.1$ was an attempt to determine whether deaths were related to weather phenomena over time. This was certainly novel in the application of multivariable time-series graphs to disease mortality, and he is likely the first to have introduced this idea in the areas of public health and epidemiology.

The prevailing theory of miasma or airborne transmission certainly responded to the direct sensory evidence of the stink of effluent discharged directly into the Thames. Farr thought that he had found the link in the strong inverse relation of mortality to elevation (Figure 4.2). As we have seen, however, he was misled by the confounding relation to water supply (Figure 4.6), and he failed to see this because of a limited graphic vision.

Like Playfair’s time-series charts, Farr’s were essentially what we call ” $1.5$ dimensional” (1.5D) -something between a univariate graph and a fully $2 \mathrm{D}$ bivariate graph. He could understand plotting $X$ (temperature,…) and $Y$ (mortality) versus time, but not the idea of plotting $Y$ versus $X$ directly, no less the idea of trying to assess the direction or strength of such relations. This was all awaiting the invention of the scatterplot and the measure of correlation, which would come later (Chapter 6).

# 数据可视化代考

## 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。

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