经济代写|发展经济学代写Development Economics代考|ECON24

2022年9月22日
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couryes-lab™ 为您的留学生涯保驾护航 在代写发展经济学Development Economics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。我们的专家在代写发展经济学Development Economics代写方面经验极为丰富，各种代写发展经济学Development Economics相关的作业也就用不着说。

• 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

经济代写|发展经济学代写Development Economics代考|Inclusiveness of Access and Use Affect the Representativeness of Big Data

Access to and use of mobiles and the Internet affect not only more traditional surveys that use these technologies but also the representativeness of data captured for big data analytics. Data captured may not reflect what a person actually thinks and does. A person may not have a data trail or data exhaust, or may be sharing or borrowing a phone or an account listed under someone else’s name. Additionally, big data sources are representative of different people and groups. For example, Twitter users tend to be younger, wealthier, more educated and more likely to live in urban areas than Facebook users, and the Twitter platform only represents a small proportion of the population, especially in low-income countries (Abreu Lopes, Bailur and Barton-Owen, 2018).

If evaluators use big data, they need to use evaluation methods that ensure that the most marginalized or vulnerable are fairly represented. In addition to considering individual access and use of mobiles and the Internet, it may be helpful to think of data as coming from four different “buckets”. This way the type and source of data can be reviewed to determine whether data is inclusive, and if not, who is missing and how can those voices be included (Raftree, 2017). Different kinds of data present more or less stark choices for organizations using them and the end evaluands and users.
1 Traditional data. In this case, researchers, evaluators and/or enumerators are in control of the process. They design a questionnaire or data gathering process and go out and collect qualitative or quantitative data; they send out a survey and request feedback; they do focus group discussions or interviews; they collect data on digital devices or on paper and digitize it later for analysis and decision-making. The sampling process is tightly controlled and is deliberately constructed to fit a predetermined criterion of quality. However, such control of the quality and soundness of the data means that it is resource-intensive and of limited size. This kind of data represents the voice of those precisely selected by the agency and those who are intended to be heard for the purpose of the evaluation.

经济代写|发展经济学代写Development Economics代考|Bias in Big Data, Artificial Intelligence and Machine Learning

The application of big data and big data analytics to development evaluation is still in its infancy. It is only in the past ten years that development and UN agencies have begun thinking about these data sources and their predictive potential, and even more recently that their role in evaluation has been examined (see Chapter 3). Though impressive capacity to process data exists, this capacity has advanced far more quickly than has human capacity to understand its implications, and ethical and legal frameworks have not yet caught up.

In her book Weapons of Math Destruction, Cathy O’Neil details several recent cases in which big data algorithms have directly caused harm, including the financial crash of the late $2000 \mathrm{~s}$, school ranking, private universities and policing. Though some big data algorithms can be healthy – baseball managers use them to devise plays – O’Neil says this is only possible if algorithms are open and transparently created, if they can be scrutinized and unpacked, if unintended consequences are tracked and adjusted for when they are negative, and if the algorithms are not causing damage or harm. Unfortunately, in many cases, those creating algorithms purposefully target and/or take advantage of more vulnerable people.

In the case of development, assuming that there is good intent, the question becomes one of the unintended consequences that could arise from creating algorithms where there is insufficient data. O’Neill notes that proxy indicators often stand in where there is an absence of hard data and can lead to perverse incentives and distortion of monitoring and evaluation systems that causes harm. If algorithms are not continuously tested and adjusted using fresh data, they can easily become stale. And if they are created by people with little contextual or cultural awareness of how a system actually works,
Predictive capabilities could go a long way towards improving development approaches and outcomes. But humans are designing the algorithms used to make these predictions, so they contain persistent and historical biases. The attractive claim of big data is that it can turn qualitative into quantitative. Yet objectivity and accuracy claims are misleading. As Boyd and Crawford (2011) note, “working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth – particularly when considering messages from social media sites”.

国际经济学代考

经济代写|发展经济学代写发展经济学代考|接入和使用的包容性影响大数据的代表性

1传统数据。在这种情况下，研究人员、评估人员和/或枚举人员控制这个过程。他们设计调查问卷或数据收集流程，然后出去收集定性或定量数据;他们会发送调查并请求反馈;他们进行焦点小组讨论或采访;他们在数字设备或纸张上收集数据，然后将其数字化，以便进行分析和决策。抽样过程是严格控制的，并刻意构造以符合预定的质量标准。然而，这种对数据质量和可靠性的控制意味着数据是资源密集型的，而且规模有限。这类数据代表了机构精确挑选的人的声音，以及为评估目的而希望听到的人的声音

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。