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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|Scatterplot Thinking

Scatterplots took visual displays and analysis beyond one-dimensional problems, passed the idea of plotting functional relations by Halley, and then passed the idea of Playfair’s time-series line graphs, where the horizontal axis was bound to time (we call this $1.5$ dimensions). The result of the scatterplot was a fully two-dimensional space, where data, depicted by points in a Cartesian framework, were free to roam, constrained only by the relations between the variables, as observed and to be explained.

The need for a scatterplot arose when scientists had to examine bivariate relations between distinct variables directly. As opposed to other graphic forms-pie charts, line graphs, and bar charts-the scatterplot offered a unique advantage: the possibility to discover regularity in empirical data (shown as points) by adding smoothed lines or curves designed to pass “not through, but among them,” so as to pass from raw data to a theory-based description, analysis, and understanding (Herschel, 1883b, p. 179).

In the toolbox of modern data graphics, the scatterplot continues to earn its keep, perhaps with a place of pride. Some of the figures in this chapter illustrate the use of modern statistical methods (regression lines, smoothing, data ellipses, and so on) to enhance perception of what should be seen in a cloud of points. It also served as a framework for graphics developers to extend the ideas of Herschel, Galton, and others to higher dimensions and more complex problems.
The advent of computer-generated statistical graphics and software beginning in the 1960s led to other new uses and enhancements. Among these was the perceptually important idea that one could trade off resolution or detail for increased multivariate scope by plotting many smaller scatterplots together in a single, coherent display, in what Tufte (1983) later referred to as “small multiples.” Onc of the first of these new ideas was the idea of a scatterplot matrix, ${ }^{35}$ a plot of all pairwise relations for $p$ variables in a $p \times p$ grid, where each subplot showed the bivariate relation between the row and column variables.

## 统计代写|数据可视化代写Data visualization代考|The Golden Age of Statistical Graphics

With these words, Howard Gray Funkhouser [1898-1984] christened this period in the last half of the nineteenth century as the “golden age of graphics.” When he wrote these lines in his $\mathrm{PhD}$ thesis at Columbia University in 1937 (which was quickly published in the history journal Osiris), he was the first modern writer to attempt a comprehensive history of the graphical representation of statistical data or to see it as a historical topic. As a historian of science, he unearthed the “volumes of forgotten lore” that constituted the érly cultivation of this topic, and álso establishéd a raison dêtree for the study of graphs as scientific objects with an intellectual history.

On many dimensions, this period Funkhouser highlighted as the Golden Age of Graphics was the richest period of innovation and beauty in the entire history of data visualization. During this time there was an incredible development of visual thinking, represented by the work of Charles Joseph Minard, advances in the role of visualization within scientific discovery, as illustrated through Francis Galton, and graphical excellence, embodied in state statistical atlases produced in France and elsewhere.

# 数据可视化代考

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## 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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