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我们提供的数据可视化Data visualization及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 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|ITM752

统计代写|数据可视化代写Data visualization代考|The Answer: A Bug

Eventually, Snow’s hypothesis was proved to be correct, but only long after his death in 1858. The causative agent, the bacillus Vibrio cholerae, was discovered with a microscope by the Italian scientist Filippo Pacini in 1854. But this finding seems to have passed largely unnoticed. The very idea that a microscopic living organism could be the cause of the disease was revolutionary and nearly unfathomable.

It wasn’t until a new cholera outbreak occurred in east London in 1866 that William Farr presented more compelling statistical evidence that this outbreak had been caused by sewage-contaminated water; but, like Snow, he had no idea of an organism-based explanation of the mechanism. It remained for the German physician Robert Koch [1843-1910] to isolate the bacillus in a pure culture in 1884, and show that the organism was always found in patients with cholera but never in those with similar symptoms (diarrhea) from other causes.

Scientific and historical appreciations often undergo mood swings. Koch justifiably received the Nobel Prize in Physiology in 1905 for his contributions, which also included the discovery of the tubercle bacillus, the main causative agent of tuberculosis. But it took until 1965 for an international committee on nomenclature to adopt Vibrio cholerae Pacini 1854 as the correct name of the cholera-causing organism.

Much later, the pendulum finally swung back to an appreciation of John Snow as the guy who got it right with the aid of a graph. Such stories, even if somewhat apocryphal, still serve a purpose. They help us to understand the connections among the hard work of data tabulation and summary, and then the effort to turn data into insightful graphical displays. But in science, it is always necessary to sell your idea to contemporaries persuasively. And nothing counts as much as a correct causal explanation that eliminates the alternatives.

统计代写|数据可视化代写Data visualization代考|Florence Nightingale’s Graphical Success

If William Farr’s beautiful radial diagram (Plate 2) had no impact because he was displaying the wrong variables, another graphical contribution by Florence Nightingale [1820-1910] to vital statistics in this period changed health policy forever. Moreover, it corrected what is now considered a blunder in the graphic portrayal of counts of deaths by Farr in this figure.

Florence Nightingale, who is widely known as the mother of modern nursing, is called “the lady with the lamp.” She was also a social reformer with a keen understanding of the power of graphics for persuasion, and consequently was also called a “passionate statistician.” 33

Nightingale was born to a wealthy, landed British family. As a young girl, she exhibited an interest in and flair for mathematics, which was encouraged by her father, William. Later, she was profoundly influenced by reading Adolphe Quetelet’s 1835 Sur L’Homme et le Developpement de ses Facultés, in which he outlined his conception of statistical method as applied to the life of man. ${ }^{34}$ She also felt a strong religious calling to the service of others, and against her mother’s strenuous objections, she decided that nursing would be her vocation.

The Crimean War, which was fought by Russia and the forces of France, Britain, and the remnants of the Ottoman Empire, began in October 1853 and lasted until February 1856. In October 1854, Nightingale appealed to her friend Sidney Herbert, secretary of state for war, to send her and a team of nurses to the Crimea. She soon recognized that most of the deaths occurred, not from battle, but from preventable causes: zymotic diseases (mainly cholera) and insufficient sanitary policy in the hospitals that treated the soldiers.

统计代写|数据可视化代写Data visualization代考|ITM752


统计代写|数据可视化代写Data visualization代考|The Answer: A Bug

最终,斯诺的假设被证明是正确的,但就在他于 1858 年去世很久之后。1854 年,意大利科学家菲利波·帕西尼(Filippo Pacini)用显微镜发现了病原体霍乱弧菌。但这一发现似乎在很大程度上已经过去了不被注意。微小的生物体可能是导致这种疾病的原因的想法是革命性的,几乎是深不可测的。

直到 1866 年伦敦东部发生新的霍乱疫情,威廉·法尔才提出更有说服力的统计证据,证明这次爆发是由污水污染的水引起的。但是,像斯诺一样,他不知道基于有机体的机制解释。1884 年,德国医生罗伯特·科赫 (Robert Koch) [1843-1910] 仍然在纯培养物中分离出这种杆菌,并表明这种生物体总是存在于霍乱患者身上,但从未在其他原因引起的类似症状(腹泻)的患者身上发现。

科学和历史鉴赏经常会经历情绪波动。科赫因其贡献而在 1905 年获得了诺贝尔生理学奖,其中还包括发现了结核病的主要病原体结核杆菌。但直到 1965 年,国际命名委员会才采用霍乱弧菌 Pacini 1854 作为引起霍乱的有机体的正确名称。


统计代写|数据可视化代写Data visualization代考|Florence Nightingale’s Graphical Success

如果威廉·法尔 (William Farr) 漂亮的径向图(图版 2)没有影响,因为他显示了错误的变量,那么弗洛伦斯·南丁格尔 (Florence Nightingale) [1820-1910] 对这一时期生命统计的另一项图形贡献永远改变了卫生政策。此外,它纠正了现在被认为是法尔在该图中对死亡人数的图形描绘中的一个错误。


南丁格尔出生于一个富裕的英国家庭。作为一个年轻的女孩,她表现出对数学的兴趣和天赋,这受到她父亲威廉的鼓励。后来,她因阅读 Adolphe Quetelet 1835 年的 Sur L’Homme et le Developpement de ses Facultés 而深受影响,其中他概述了他将统计方法应用于人类生活的概念。34她也感受到了一种强烈的宗教呼唤,即为他人服务,并且在她母亲的强烈反对下,她决定以护理为职业。

1853 年 10 月,俄罗斯与法国、英国和奥斯曼帝国的残余势力展开了克里米亚战争,一直持续到 1856 年 2 月。1854 年 10 月,南丁格尔向她的朋友、国务卿西德尼·赫伯特求助为了战争,把她和一队护士送到克里米亚。她很快就意识到,大多数死亡不是来自战斗,而是来自可预防的原因:酵母菌病(主要是霍乱)和治疗士兵的医院卫生政策不足。

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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。