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数据可视化是信息和数据的图形化表示。通过使用像图表、图形和地图这样的视觉元素,数据可视化工具提供了一种方便的方式来查看和理解数据的趋势、异常值和模式。
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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|A perception of objectivity
According to Anthony McCosker and Rowan Wilken (2014), data visualizations often offer a ‘fantasy of knowing’ or of ‘total knowledge’, or in Donna Haraway’s words, they claim to present a ‘God’s eye view’ (Haraway, 1988, p. $5^{81}$ ). The use of a data visualization in a newspaper article or a corporate report carries with it a rhetorical weight: the simple presence of the data visualization seems to state ‘Look, we have data. This is true’ (see Tal, Aner, \& Wansink, 2016 on data visualization’s association with truthfulness).
José van Dijck uses the term dataism to describe the ideology of big data, which is characterized by ‘a widespread belief in the objective quantification and potential tracking of all kinds of human behavior and sociality through online media technologies’ (2014, p. 198). Epistemologically, data visualizations build upon this trust in data.
We can trace many histories of society’s growing trust in numbers. The registration of data about crimes and criminals mentioned above tells of one such history, which can be traced forwards to today’s bodycams, surveillance, and biometrics (Gates, 2011). Another, parallel history is that of the transition from midwives and their home-based care of mothers and infants to the increasing medicalization of prenatal care. This story can be told as a transfer of power from women to men, but it can also be seen as a transfer of trust from humans to machines, as the increasing institutionalization of prenatal and infant care included a radical growth in the use of technology to monitor growth and health (Oppenheimer, 2013). Today, iPhone apps connect to digital scales that generate daily data visualizations of a baby’s weight (Rettberg, 2014, p. 67 ) and smart socks generate continuous visualizations of a baby’s heartbeat (Leaver, 2017).
The management of birth is one thread in this story of numbers. Another thread is the management, or perhaps rather the marketing, of instruments of death, as told by Donald Mackenzie in Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (1993). Or we might consider the prevention of life, a thread of the story told by Michelle Murphy in The Economization of Life (2017), where she discusses how demographic models comparing population size and financial growth created programmes intended to improve the future economies of developing countries through extensive birth control and abortion programmes.
统计代写|数据可视化代写data visualization代考|The average as norm
Displaying data visually rather than as a table of numbers is a powerful method for finding patterns in the data. Some patterns recur in many different datasets, such as the bell-shaped curve seen in Figure 2.1, a graph showing the heights of Belgian men, which follows what is mathematically known as a normal distribution. Writing in the 1860 , Adolphe Quetelet interpreted this recurrence as evidence of a fundamental social law, and defined the central portion of the curve as ‘normal’, with those outside the normal zone seen as aberrations (1997). Sekula explains that ‘[t]hus conceived, the “average man” constituted an ideal, not only of social health, but of social stability and of beauty’ (1986, p. 22). Quetelet’s work leaned heavily upon data visualizations. He first showed his data in the form of a table, then showed it visualized, drawing conclusions from the patterns that became apparent when the numbers were shown as curves on an $x$ – and $y$-axis.
The power of visualizations to show averages and patterns contributed to the nineteenth-century privileging of the ‘norm’, or as Lennard Davis argues, a ‘generalized notion of the normal as an imperative’, where ‘the average then paradoxically becomes a kind of ideal, a position to be wished’ (Davis, 2013, p. 2). This privileging of the average is a marked break from earlier traditions that saw the ideal body, represented for instance in paintings of Venus, as something ‘mytho-poetical’, a ‘divine body’ that is ‘not attainable by a human’ (Davis, 2013, p. 2).
As it turns out, the average human doesn’t exist. Yes, that even curve shape shown in Figure $2.1$ does show up again and again when you measure almost any aspect of humans – or of most things, really. But that doesn’t mean that any individual human is ‘average’. In her book Technically Wrong (2017), Sara Wachter-Boettcher tells the story of how the adjustable seatbelt was designed. Prior to its invention, the air force planned to design cockpits that fit ‘the average pilot’-but they discovered that none of their pilots were of average size in all the ten dimensions they measured, such as height, wrist circumference, and shoulder width. Wachter-Boettcher uses this point to argue that it’s important to design technology that fits people at each extreme rather than for the average person, as the air force did by creating adjustable seats and seat belts (Wachter-Boettcher, 2017). The idea of ‘the average’ may be encouraged by data visualizations, but that doesn’t mean that it’s necessarily the most useful way of viewing the data.
统计代写|数据可视化代写data visualization代考|Correlation is easier than causation
Computers are extremely good at finding correlations. In fact, this is one of the mainstays of current models of deep machine learning, where software is fed ‘big data’ and works through it to find patterns. By analysing historical data, computers can find patterns that allow them to predict future behaviour. Often these predictions are eerily accurate. In some tests, AI systems do a better job at medical diagnosis than human doctors (Olson, 2018). It is wise to remember, though, that many stakeholders have a strong financial interest in convincing the general public that AI is efficient, perhaps more efficient than humans, and AI’s ability to make accurate predictions is often overstated.
Visualizations of data also prioritize correlation over causation. They show patterns and relative size or position, but it is more difficult to show
causality. Viktor Mayer-Schönberger and Kenneth Cukier argue in their book Big Data (2013) that we no longer need causality. If we can predict how likely patients are to take their medicine based on their car insurance payment history, why would we want or need to know why they don’t take their medicine, Mayer-Schönberger and Cukier ask. Obviously their payment history doesn’t cause their tendency to take or not take medicines as prescribed. But it no longer matters. Causality for them is simply hhuman intuiting’ that doesn’t deepen our insight, it is merely a ‘cognitive shortcut that gives us the illusion of insight but in reality leaves us in the dark about the world around us’ (2013, p. 64). Others are more concerned about the downplay of causality, as Wendy Chun writes: ‘Big data […] offers a form of cognitive mapping that allegedly sees all, by ignoring causes’ (2017, p. 56 ).
Different forms of representation emphasize different relationships and patterns. Quetelet’s data visualizations contributed to the idea of the average as something to be sought after, whereas earlier forms of representation, such as paintings, were well-suited to representing ideal beauty as something beyond human perfection. Data visualizations prioritize correlation. Narrative, by contrast, is a form of representation that often emphasizes causal connections. Narratives organize events in time. Some also provide causal connections between the events. E. M. Forster argues that such connections separate a story, which is just events in time (‘and then, and then’), from a plot, which adds causality. “The king died and then the queen died,” is a story. “The king died, then the queen died of grief” is a plot,’ Forster wrote (1949, p. 82). Roland Barthes, on the other hand, argued that ‘the mainspring of narrative’ is the reader’s assumption that an event that happens after another event is caused by the first event, meaning that ‘narrative would be a systematic application of the logical fallacy […] post hoc, ergo propter hoc’ (1977, p. 94). Causation is not always evident, but different forms of representation emphasize causation or correlation in different ways. Visualizations do not usually portray narratives, although this is certainly possible, as discussed by Wibke Weber and others in this volume.
数据可视化代写
统计代写|数据可视化代写data visualization代考|A perception of objectivity
根据 Anthony McCosker 和 Rowan Wilken (2014) 的说法,数据可视化通常提供“了解的幻想”或“全面了解”,或者用 Donna Haraway 的话说,他们声称呈现了“上帝的视角”(Haraway,1988,p .581)。在报纸文章或公司报告中使用数据可视化带有一种修辞的重要性:数据可视化的简单存在似乎表明“看,我们有数据。这是真的”(参见 Tal、Aner、\& Wansink,2016 年关于数据可视化与真实性的关联)。
José van Dijck 使用术语数据主义来描述大数据的意识形态,其特点是“普遍相信通过在线媒体技术对各种人类行为和社会性进行客观量化和潜在跟踪”(2014 年,第 198 页) . 从认识论上讲,数据可视化建立在对数据的这种信任之上。
我们可以追溯社会对数字越来越信任的许多历史。上面提到的犯罪和罪犯数据的登记讲述了这样一个历史,可以追溯到今天的身体摄像头、监视和生物识别技术(Gates,2011)。另一个平行的历史是从助产士及其对母婴的家庭护理向日益医学化的产前护理过渡的历史。这个故事可以说是权力从女性转移到男性,但也可以看作是信任从人类转移到机器,因为越来越多的产前和婴儿护理制度化包括技术使用的激进增长监测生长和健康(Oppenheimer,2013)。今天,iPhone 应用程序连接到数字秤,生成婴儿体重的每日数据可视化(Rettberg,2014,p.
出生管理是这个数字故事的一个主线。另一个线索是死亡工具的管理,或者更确切地说是营销,正如唐纳德麦肯齐在《发明准确性:核导弹制导的历史社会学》(1993)中所说。或者我们可以考虑预防生命,这是 Michelle Murphy 在《生命的经济化》(2017 年)中讲述的故事线索,其中她讨论了比较人口规模和金融增长的人口模型如何创建旨在改善发展中国家未来经济的计划通过广泛的节育和堕胎计划。
统计代写|数据可视化代写data visualization代考|The average as norm
直观地显示数据而不是数字表是在数据中查找模式的有效方法。一些模式在许多不同的数据集中重复出现,例如图 2.1 中的钟形曲线,该曲线显示了比利时男性的身高,它遵循数学上所谓的正态分布。Adolphe Quetelet 在 1860 年的著作中将这种重现解释为基本社会规律的证据,并将曲线的中心部分定义为“正常”,将正常区域之外的部分视为异常(1997)。Sekula 解释说,“他认为,“普通人”不仅是社会健康的理想,而且是社会稳定和美丽的理想”(1986 年,第 22 页)。Quetelet 的工作很大程度上依赖于数据可视化。他首先以表格的形式显示他的数据,然后将其可视化,X- 和是-轴。
可视化显示平均值和模式的力量促成了 19 世纪“规范”的特权,或者正如 Lennard Davis 所说,“将正常作为一种命令的普遍概念”,其中“平均值然后自相矛盾地变成了一种理想,一个理想的位置”(戴维斯,2013 年,第 2 页)。这种对平均值的重视与早期传统的明显不同,早期传统认为理想的身体,例如在维纳斯的画中,是一种“神话般的诗意”,一种“人类无法获得的”“神圣的身体”(戴维斯,2013 年,第 2 页)。
事实证明,普通人并不存在。是的,如图所示的那个均匀曲线形状2.1当你测量人类的几乎任何方面——或大多数事物,真的时,它确实会一次又一次地出现。但这并不意味着任何个人都是“平均的”。Sara Wachter-Boettcher 在她的著作 Technically Wrong (2017) 中讲述了可调节安全带的设计故事。在其发明之前,空军计划设计适合“普通飞行员”的驾驶舱——但他们发现他们的飞行员在身高、腕围和肩宽等十个维度上都没有平均身材. Wachter-Boettcher 使用这一点认为设计适合每个极端的人而不是普通人的技术很重要,就像空军通过创建可调节座椅和安全带所做的那样(Wachter-Boettcher,2017 年)。数据可视化可能会鼓励“平均值”的想法,
统计代写|数据可视化代写data visualization代考|Correlation is easier than causation
计算机非常擅长寻找相关性。事实上,这是当前深度机器学习模型的支柱之一,其中软件被输入“大数据”并通过它来寻找模式。通过分析历史数据,计算机可以找到能够预测未来行为的模式。这些预测通常出奇地准确。在某些测试中,人工智能系统在医疗诊断方面的表现优于人类医生(Olson,2018 年)。不过,明智的做法是,许多利益相关者在说服公众相信人工智能是高效的,或许比人类更高效,而人工智能做出准确预测的能力往往被夸大了,这对他们有着强烈的经济利益。
数据的可视化也优先考虑相关性而不是因果关系。它们显示模式和相对大小或位置,但更难显示
因果关系。Viktor Mayer-Schönberger 和 Kenneth Cukier 在他们的著作 Big Data (2013) 中提出,我们不再需要因果关系。Mayer-Schönberger 和 Cukier 问道,如果我们可以根据他们的汽车保险支付历史预测患者服药的可能性,我们为什么想要或需要知道他们为什么不服药。显然,他们的付款历史不会导致他们倾向于服用或不服用处方药。但这不再重要。对他们来说,因果关系只是人类的直觉”,它不会加深我们的洞察力,它只是一种“认知捷径,让我们产生洞察力的错觉,但实际上让我们对周围的世界一无所知”(2013,第 64 页) )。其他人则更关心因果关系的淡化,正如 Wendy Chun 所写:“大数据 [……] 提供了一种据称可以看到一切的认知映射形式,
不同的表现形式强调不同的关系和模式。Quetelet 的数据可视化有助于将平均值视为值得追捧的东西,而早期的表现形式(如绘画)非常适合将理想美表现为超越人类完美的东西。数据可视化优先考虑相关性。相比之下,叙事是一种经常强调因果关系的表现形式。叙事及时组织事件。有些还提供了事件之间的因果关系。EM Forster 认为,这种联系将故事与情节分开,故事只是时间上的事件(“然后,然后”),情节增加了因果关系。“国王死了,然后王后也死了”,这是一个故事。“国王死了,然后王后悲痛而死”是一个阴谋,”福斯特写道(1949 年,第 82 页)。另一方面,罗兰·巴特认为,“叙事的主要动力”是读者假设在另一个事件之后发生的事件是由第一个事件引起的,这意味着“叙事将是逻辑谬误的系统应用 [… ] post hoc, ergo propter hoc’ (1977, p. 94)。因果关系并不总是显而易见的,但不同的表现形式以不同的方式强调因果关系或相关性。可视化通常不描绘叙述,尽管这当然是可能的,正如 Wibke Weber 和其他人在本卷中所讨论的那样。ergo propter hoc’(1977 年,第 94 页)。因果关系并不总是显而易见的,但不同的表现形式以不同的方式强调因果关系或相关性。可视化通常不会描绘叙事,尽管这当然是可能的,正如 Wibke Weber 和其他人在本卷中所讨论的那样。ergo propter hoc’(1977 年,第 94 页)。因果关系并不总是显而易见的,但不同的表现形式以不同的方式强调因果关系或相关性。可视化通常不会描绘叙事,尽管这当然是可能的,正如 Wibke Weber 和其他人在本卷中所讨论的那样。
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金融工程代写
金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。
非参数统计代写
非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。
广义线性模型代考
广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。
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)。
有限元方法代写
有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。
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。
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随机分析代写
随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。
时间序列分析代写
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。
回归分析代写
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,工具箱允许您学习和应用专业技术。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。