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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Ages in the History of Graphics

One convenient way to appreciate the development of ideas and techniques in any field is to record and document the significant events in its history. This is basically what Funkhouser started in his written history of graphical methods.

The Milestones Project, www.datavis.ca/milestone, ${ }^1$ does much more. It is a comprehensive online repository for this history, with representative images, references, and text descriptions that can be searched and displayed in various ways and can also analyzed as data on this history.

Figure $7.1$ gives a graphic overview, showing the time course of these events from 1500 to the present by a smoothed curve of relative frequency (a kernel density estimate) and fringe marks (a rug plot) at the bottom for the discrete milestone events.

The dashed lines and labels for various periods reflect one convenient parsing of this history.? Of interest here is the rapid rise in the early 1800s, which peaked later in this century, followed by a steep decline in the early 1900 s, before an even more dramatic rise in the last half of the 1900s.

The first half of the nineteenth century, labeled “Begin modern period” in this graph, is the same historical period described in Chapter 3 as the Age of Data and the time period in which Playfair invented his chart and graphic forms and Dupin, Guerry, and others first used shaded maps to show the geographic distribution of socially important data.

## 统计代写|数据可视化代写Data visualization代考|Prerequisites for the Golden Age

As we discussed in Chapters 3 and 4 , one critical development that launched the invention of the basic forms of statistical graphics in the early part of the nineteenth century was widespread collection of data on social problems (crime, suicide, poverty) and disease outbreaks (cholera). In a number of key cases, graphical methods proved their utility, sometimes suggesting explanations or solutions. A second general group of advances that enabled the Golden Age concerned technology, for (a) reproducing and publishing data graphics using color, (b) recording raw data for more than one variable at a time, and (c) tabulating or calculating some summaries that could then be displayed in graphs. A few of these are illustrated in Figure 7.2.

In the period leading up to the Golden Age, thematic maps and diagrams had been printed by copperplate engraving. With this technique, an image is incised on a soft copper sheet, then inked and printed. In the hands of master engravers and printers, copperplate technology could easily accommodate fine lines, small lettering, stippled textures, and so forth. The works of Albrecht Dürer and other engravers attest to how hand-drawn artwork could be transformed into something that captured the artist’s intent, with fine lines and texture, and then be printed in many copies. Farly data graphic works in this period featured both the author and the engraver in captions or legends, because both had contributed to the final product. woodcut methods. But copperplate was slower, more costly, and required different print runs if color was to be used in an overlay inked with a different color. The graphs in Playfair’s major works (Playfair, 1786, 1801), for example, were printed via copperplate but hand-colored (often by Playfair himself): hence they were printed in limited numbers.

Lithography, a chemical process for printing invented in 1798 by Aloys Senefelder [1771-1843], allowed much longer print runs of maps and diagrams than engraving, was far less expensive, and also made it easier to achieve fine tonal gradation in filled areas.

By around 1850 , lithographic techniques were adapted to color printing, making the use of color less expensive and more frequent. More importantly, color could be more easily used as an important perceptual feature in the design of thematic maps and statistical diagrams; high-resolution color printing is an important characteristic of the Golden Age. ${ }^4$

# 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|图形历史中的年龄

.

19世纪上半叶，在这张图中被标记为“开始现代时期”，与第三章中描述的数据时代是同一历史时期，也是普莱费尔发明图表和图形形式的时期，也是杜邦、格里和其他人首次使用阴影地图来显示社会重要数据的地理分布的时期

## 统计代写|数据可视化代写数据可视化代考|黄金时代的先决条件

. 正如我们在第3章和第4章中所讨论的那样，19世纪早期推动统计图形基本形式发明的一个关键发展是广泛收集社会问题(犯罪、自杀、贫困)和疾病爆发(霍乱)的数据。在一些关键的案例中，图形方法证明了它们的效用，有时提供了解释或解决方案。使黄金时代成为可能的第二大进步是技术方面的进步，包括:(A)使用彩色复制和发布数据图表，(b)一次记录多个变量的原始数据，以及(c)制表或计算一些概要，然后以图表的形式显示出来。其中一些在图7.2中进行了说明 在黄金时代之前的时期，专题地图和图表已经用铜版雕刻印刷出来。用这种技术，图像被雕刻在柔软的铜片上，然后涂上墨水并打印出来。在雕刻大师和印刷大师的手中，铜版技术可以轻松地容纳细线、小字体、点状纹理等。阿尔布雷希特(Albrecht Dürer)和其他雕刻师的作品证明，手绘艺术品是如何通过精细的线条和纹理，转化成能够捕捉艺术家意图的东西，然后被印制成许多副本的。这一时期的早期数据图形作品在标题或图例中都有作者和雕刻者的特征，因为他们都对最终产品做出了贡献。木刻方法。但铜版印刷速度较慢，成本较高，如果要在涂有不同颜色的覆盖层上使用颜色，则需要不同的印刷运行。例如，普莱费尔的主要作品(普莱费尔，1786,1801)中的图表是用铜板印刷的，但手工上色(通常是普莱费尔自己上色):因此它们的印刷数量有限 光刻术是由Aloys Senefelder[1771-1843]在1798年发明的一种化学印刷工艺，它可以比雕刻术印刷更长时间的地图和图表，而且远不昂贵，而且也更容易在充满的区域实现精细的色调渐变 大约在1850年，平版印刷技术被用于彩色印刷，使得彩色的使用更便宜，更频繁。更重要的是，在专题地图和统计图表的设计中，颜色可以更容易地作为一个重要的感知特征;高分辨率彩色印刷是黄金时代的一个重要特征。${ }^4$

## 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。

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