经济代写|发展经济学代写Development Economics代考|ECON4560

statistics-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
• (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|What Is Likely to Happen

Predictive analysis uses patterns of associations among variables to predict future trends. The predictive models are sometimes based on Bayesian statistics and identify the probability distributions for different outcomes. Other approaches draw on the rapidly evolving field of machine learning (Alpaydin, 2016). When real-time data is available, predictions can be continuously updated. Predictive analytics can use social media data. In Indonesia, for example, although Internet penetration is lower than in other Southeast Asian countries (18\% in 2014), analysis of tweets has been used to provide a rapid, economic way to assess communicable disease incidence and control (UN Global Pulse, 2015).

In the United States, predictive analytics are currently used by commercial organizations and government agencies to predict outcomes such as which online advertisements customers are likely to click on, which mortgage holders will prepay within 90 days, which employees will quit within the next year, which female customers are most likely to have a baby in the near future and which voters will be persuaded by political campaign contacts (Siegel, 2013).

A key feature of many of these applications is that the client is only interested in the outcome (how to increase click-rates for online advertising) but without needing to know “why” this happens. In contrast, it is critical for development agencies to understand the factors that determine where, why and how outcomes occur, and where and how successful outcomes can be replicated in future programmes. So, there is a crucial distinction between generating millions of correlations, and methods to determine attribution and causality.

Typical public-sector applications include the most likely locations of future crimes (“crime hot-spots”) in a city, which soon-to-be-released prisoners are likely to be recidivists and which are likely to successfully be re-integrated into society, and which vulnerable youth are most likely to have future reported incidents at home or at school (Siegel, 2013) and predicting better outcomes for children in psychiatric residential treatment (Gay and York, 2018). Box $3.1$ provides another example of how predictive analytics was used to predict which groups of children within a child welfare system are most likely to have future reported incidents of abuse or neglect in the home (Schwartz et al., 2017). For all of these kinds of analysis, it is essential to understand the causal mechanisms determining the outcomes, and correlations, which only identify associations, without explaining the causal mechanisms, are not sufficient.

经济代写|发展经济学代写Development Economics代考|The Data Continuum

When discussing development evaluation strategies, it is useful to distinguish between big data, “large data” (including large surveys, monitoring data and administrative data such as agency reports) and “small data” (the kinds of data generated from most qualitative and in-depth case study evaluations and supervision reports).

At the same time, the borders between the three categories are flexible and there is a continuum of data, rather than distinct categories (Figure 3.3), and the lines between the three are less well defined. For example, for a small NGO, a beneficiary survey covering only several hundred beneficiaries would be considered small data, whereas in a country such as India or China, surveys could cover hundreds of thousands of respondents. A further complication arises from the fact that several small datasets might be merged into an integrated data platform so that the integrated dataset might become large.

There is a similar continuum of data analysis. While many kinds of data analytics were developed to analyse big data, they can also be used to analyse large or even small datasets. Mixed method strategies refer to the combining of different kinds of data and of different kinds of analysis. Consequently, data analytics approaches that are designed for large datasets can also be used to analyse smaller datasets, while qualitative analysis methods, designed originally for small datasets, can also be applied to big data and large data. For example, a national analysis of national and international migration in response to drought (big or large data) might elect a few areas of origin for the preparation of descriptive, largely qualitative, case studies.

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。