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statistics-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 Birth of Data

In one of the first published cookbooks, around 1860, Mrs. Isabella Beeton began her recipe for rabbit stew with the instruction: “First, catch a rabbit.” So too an early, prescient recipe for data graphics might have begun, “First, get some data.” The second step in the recipe might have been, “Now, make some sense of it!”

Slightly later (1891), Arthur Conan Doyle had Sherlock Homes proclaim in Scandal in Bohemia, “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” These popular ideas set a theme for this chapter: the connections between observations, quantified as “data,” and conclusions based on the evidence those observations provide, facilitated by graphs for discovery and communication.

The idea of deriving knowledge through observation and experience, as opposed to inner thought, starts, in Western tradition, with Aristotle’s view that all knowledge comes through our sensory experience: our concepts of an apple or a tree are derived over time through numerous encounters with examples from which we learn the essential features. Aristotle made this idea concrete with the notion of the human mind as a blank slate (tabula rasa) on which experience records its marks.

But this idea did not really gain adherents until the rise of British empiricism (with John Locke, George Berkeley, and David Hume) and the Age of Reason in the seventeenth and eighteenth centuries. In part, the prior lack of empirical data accounts for the gap in innovation of graphical methods between van Langren in the early seventeenth century and the explosion of graphical methods in 1780-1840 as we saw in Figure 2.3.

The systematic and widespread collection of data developed steadily over this time in response to important issues in astronomy (the “shape” of the Earth, orbits of planets), political economy (new markets, balance of trade), and social factors (literacy, crime). These and other areas provided the essential ingredients for Mrs. Beeton’s recipe for graphs: just as gastronomy or hunger, together with availability of rabbits, may have driven the recipe for rabbit stew, so too did important scientific questions propel the collection of empirical data in order to refine concepts or test competing views against each other.

## 统计代写|数据可视化代写Data visualization代考|Early Numerical Recordings

The recording of numbers that could be called “data” (under a loose definition) goes back to antiquity. For 7,000 years prior to construction of the Aswan High Dam, people lived and farmed along the Nile. One early and welldocumented source are the records of the times and heights of the flooding of the Nile, which today is still celebrated in Egypt for two weeks starting August 15 as the holiday Wafaa El-Nil. When Herodotus began writing about Egypt and the Nile (circa $450 \mathrm{BCE}$ ), the Egyptians, who knew that their prosperity depended on the river’s annual overflow, had been keeping records of the Nile’s high water mark for more than three millennia. In 1951, Popper presented a time series of the Nile flood levels over thirteen centuries, from AD 622 to 1922 , which was perhaps the longest time series ever recorded.

However, we should not consider this as evidence of any sort because there was no sense that what happened in past years could be considered as an aggregate collection of numbers you could do anything more generally useful with. If you were a farmer on the Nile, you probably knew the date and level of flooding for the last year or so. But this gave only a little help in deciding when to plant or whether you could afford to buy another ox five years later. The historical record, as detailed as it was, comprised just a collection of individual numbers that were seen through a close-up lens of the very recent past. Certainly no one thought to make a chart of the high water level over time or attempt to compare the average water level in the last decade to what might occur in the next.

Another old, and extremely detailed, source of numerical recording are the so-called ephemeris tables (from the Latin and Greek words meaning “diary” or “calendar”), giving the positions of astronomical objects (the moon, stars, and planets) in the sky at given geographic positions, at regular intervals of date and time. After his coronation in January 1252, King Alfonso $\mathrm{X}$ of Castile commissioned a new, more accurate and detailed set of tables, the Alfonsine Tables, giving data for calculating the position of the sun, moon, and planets relative to fixed stars (Figure 3.1). The first printed version did not appear until 1483 .

## 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