## 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

tatistics-lab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。

## 经济代写|发展经济学代写Development Economics代考|CVEN5838

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|Demystifying Big Data

The world is more connected, interdependent and data-rich than ever. Exponential growth in the volume of data produced globally means that $90 \%$ of all the data in existence today has been generated during the past two years. The explosion of digital services over the past decade has allowed many to become producers, owners and consumers of data. Between 2005 and 2015 , the number of Internet users more than tripled from 1 billion to $3.2$ billion. More households now own a mobile phone than have access to electricity or clean water (World Bank, 2016).

The exponential growth of big data and data analytics provides information and analytical capacity that would have been unimaginable even a few years ago. These ‘digital dividends’ include a trove of real-time information on many issues, such as trends in food prices, availability of jobs, access to health care, quality of education and reports of natural disasters. Alongside these benefits, the digital divide continues to pose challenges (World Bank, 2016).

All of these developments have important applications for research and planning. Biometric data generated from the IoT has produced a rapidly evolving research area on the “quantified self” (Wolf, 2010). Sentiment analysis, sociometric analysis, digital spatial analysis and other tools have made possible research on the “quantified community” (Kontokosta, 2016). Satellite images, social media analysis, cell-phone data on traffic patterns and many other sources have contributed to new fields of research on city planning, urban development, migration and poverty analysis (Ashton, Weber and Zook, 2017). Satellite images and remote sensors have greatly advanced climate research.

## 经济代写|发展经济学代写Development Economics代考|Defining Big Data and NIT

It is useful to distinguish between big data and the broader concept of NIT, which combines big data, ICT and the rapidly growing field of the IoT (Figure 3.1). It is also important to distinguish between primary big data (e.g. the original satellite images, Twitter feeds or electronic records of phone calls and financial transfers), which can involve many millions of digital records, and the processed versions of these original data. Whereas handling primary big data requires access to massive computing facilities, data processing transforms the original data into formats that can be accessed and used on personal computers and handheld devices. When it is stated that big data is fast and economical to use, this refers to the processed data, which may have been very expensive to produce from the primary big data.

Big data is often defined in terms of the ” $3 \mathrm{Vs} “:$ the velocity with which big data can be collected, analysed and disseminated; the volume of data that can be generated; and the variety of data covering different topics and using different formats. The continued rapid growth of information technology means that definitions based on each of these dimensions rapidly become out of date. Data that only a few years ago would have required a large data centre to process are now easily downloadable on smartphones and personal computers. A fuller definition of big data must include its differences from conventional survey data in terms of costs and computing requirements, coverage of population and granularity, breadth of contextual data, potential sample bias, ease of dissemination and access, relevance to a particular purpose, use of time series data, integration of multiple data sources, and creation of qualitative data (Table 3.1).

## 有限元方法代写

tatistics-lab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。

## 经济代写|发展经济学代写Development Economics代考|EC982

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|Big Data Analytics and Development Evaluation

New information technology (NIT), comprising big data, information and communication technologies (ICTs) and the Internet of things (IoT), is transforming every aspect of life in both industrial and developing nations. NIT signifies the nature of interaction between its three aspects mentioned above. The emergence of big data is closely linked to advances in ICTs. In today’s hyper-connected digital world, people and things leave digital footprints in many different forms and through ever-increasing data flows. They originate from commercial transactions; records that companies and governments collect about their clients and citizens; user-generated online content such as photos, videos, tweets and other messages; and traces left by the IoT, that is, by uniquely identifiable objects whose activity can be tracked (ITU, 2014).

The IoT consists of devices connected to the Internet anytime and anywhere. In its most technical sense, it consists of integrating sensors and devices into everyday objects that are connected to the Internet over fixed and wireless networks. The fact that the Internet is present at the same time everywhere makes mass adoption of this technology more feasible. Given their size and cost, the sensors can easily be integrated into homes, workplaces and public places. In this way, any object can be connected and can “manifest itself” over the Internet. Furthermore, in the IoT, any object can be a data source (Accenture and Bankinter, 2011).

Any talk of ICT4Eval is incomplete without speaking of NIT. The interaction between the new generation of ICT tools, such as sensors, devices and software, the voluminous data they are producing and processing, and their potential use lies at the heart of this book’s endeavour. ICTs encompass the various technologies and accompanying devices that exist, IoT signifies the new ways in which these technologies and devices will interact with one another, while big data is what is produced in the process of using the first two. Given that evaluations are concerned with data and its conversion to information and knowledge, this chapter will focus predominantly on big data, its generation and usage, and occasional references to NIT.

There is enormous potential for integrating NIT into the design of development evaluation, but evaluation offices have been much slower to adopt NIT than have their operations colleagues. The methodological, organizational and political reasons for this slow adoption need to be understood and overcome so that development evaluation can adapt to the rapidly evolving NIT ecosystem. The wide range of available NIT tools and techniques can also be used to address the main challenges affecting conventional evaluation methodologies and strengthen evaluation designs.

## 经济代写|发展经济学代写Development Economics代考|Some Themes from the Big Data Literature

While not attempting to provide a comprehensive literature, this section identifies some of the evolving themes in the big data and development literature that are discussed in this chapter. Over the past five years, there has been a discovery of “big data” by the business community, the media and the international development community. A number of popular publications such as Marr’s (2015) “Big data: Using smart big data to analytics and metrics to make better decisions and improve performance” and Siegel’s (2013) “Predictive analytics: The power to predict who will click, buy, lie or die” promote the powerful new tools of big data and data analytics to the business world. Around the same time, an article on the Bloomberg website (2015) illustrates how the potential of specific big data technologies such as satellite images could be harnessed as tools for business analysis.

One of the first broad discussions of the implications for international development was the 2012 White Paper “Big Data for Development” published by UN Global Pulse and edited by Emmanuel Letouzé. While UN Global Pulse had already conducted a number of proof-of-concept studies on the applications of big data in many areas of development (unglobalpulse.org), these proof-of-concept studies were still only known to a relatively small audience. The White Paper introduced the development community to the concepts of big data and its many applications in development. Letouzé, Arieias and Jackson published an updated version of the paper in 2016 which covered the genesis of big data, the big data ecosystem, theoretical considerations and controversies, institutional and cultural considerations, and practical applications. While these publication introduced big data to the official development aid sector, Patrick Meier’s (2015) “Digital humanitarians: How big data is changing the face of humanitarian response” became one of the early reference sources on the dramatic ways in which big data was being adopted and changed by humanitarian non-profit agencies. Beginning with the Haitian Earthquake, Meier presents a series of well-documented cases on the role of big data in emergency relief, monitoring and criticizing political regimes, and as a tool to promote social and political change. There are now increasing numbers of websites and conferences dedicated to the applications of big data and particularly ICT, in the latter case driven by the dramatic advances in wireless technology, in development. ${ }^1$ These are complemented by exponentially increasing numbers of free and commercially available apps covering every aspect of development.

## 有限元方法代写

tatistics-lab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。

## 经济代写|发展经济学代写Development Economics代考|EC982

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|The Greatest Opportunity

This is the first time in the history of international development that the world’s heads of state have committed to follow up and review mechanisms to assess the implementation of global goals. This assessment takes the form of voluntary national reviews (VNRs) to be undertaken by national governments of their progress on SDGs. One hundred and eleven VNRs have been presented at the UN High-Level Political Forum on Sustainable Development (HLPF) since 2016, with a further 51 due to be presented in 2019 (UNDESA, 2018). This high-level and far-reaching commitment could enable a surge in the demand for country-led evaluation. Key policymakers will hopefully demand their own national evaluation systems, so that they can produce high-quality evaluations to inform the national SDG reviews that countries will be presenting at the UN HLPF. This is therefore an unprecedented opportunity for the evaluation community. On the other hand, evaluation of these broad-reaching goals with a central focus on “no one left behind” presents several challenges:

• How do we assess whether development interventions are relevant, and are having an impact in decreasing inequality and improving the welfare of the worst-off groups?
• How do we carry out evaluation given the complexity of the SDGs? Are we going to evaluate complex and inter-dynamic environments with the traditional linear, simple and static logical framework (logframe) approach?
• How can we take advantage of new technologies to address the challenges above?
• Most importantly, how can we strengthen the capacities of governments, civil society organizations (CSOs) and parliamentarians to evaluate whether interventions are producing equitable outcomes for marginalized populations?

Below are some suggestions about how to address these challenges while capitalizing on the great opportunity the SDGs provide to all of us.

## 经济代写|发展经济学代写Development Economics代考|How Do We Carry Out Evaluation Given

As mentioned above, the SDGs are interrelated and interlinked, which adds to their complexity but also to their dynamic interaction and transformational impact. As the map of the SDGs produced by Le Blanc (2015) illustrates clearly, the SDGs and their targets can be seen as a system in which the goals are linked through targets that refer to multiple goals. The map of the SDGs shown in Figure $1.3$ produced by Le Blanc (2015) represents the first 16 SDGs as broader circles of differing colours, while targets are represented by smaller circles in the colour of the goal under which they figure. The map conveys a clear sense that SDGs are a system, with goals and targets interlinked.

An additional perspective shows the strengths of the links among the goals (Figure 1.4). The thicker the link between two goals, the more targets are linking the two goals, directly or through a third goal. The thickest links are between gender and education (SDGs 4 and 5), and between poverty and inequality (SDGs 1 and 10), demonstrating once again the centrality of the principle of “leaving no one behind”. There are also strong connections between SDGs 10 and 16, on peaceful and inclusive societies.

Many targets referencing inequality are listed under other goals (Figure 1.5). Of note is the strong link between inequality and peaceful and inclusive societies (SDG 16), with no fewer than six targets explicitly linking the two, including two from SDG 5 on gender. As can be seen in Figure 1.4, the largest number of links (9) is with the poverty goal.

## 经济代写|发展经济学代写Development Economics代考|The Greatest Opportunity

• 我们如何评估发展干预措施是否相关，以及是否对减少不平等和改善最贫困群体的福利产生影响？
• 鉴于可持续发展目标的复杂性，我们如何进行评估？我们是否要使用传统的线性、简单和静态逻辑框架（logframe）方法来评估复杂和交互动态的环境？
• 我们如何利用新技术来应对上述挑战？
• 最重要的是，我们如何加强政府、民间社会组织 (CSO) 和议员的能力，以评估干预措施是否为边缘化人群产生了公平的结果？

## 有限元方法代写

tatistics-lab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。

## 经济代写|发展经济学代写Development Economics代考|ECON3110

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|ICTs with Promise for Evaluators

The conference focused on four broad technical strands of discussion: wireless communication, remote sensing, machine learning and big data, as well as exploring cross-cutting issues such as ethics and privacy. These technologies are not new – most have existed for decades. Artificial intelligence and machine learning originated in the 1950s with Alan Turing’s “Turing test”. Remote sensing was developed during the space race of the Cold War era. Wireless communication and the Internet grew out of technologies developed for military applications decades ago. Thus, the technologies discussed in this book might not be novel by themselves. However, what is new is their proliferation, access and relative affordability. They have evolved over a period of time to lend themselves to use for the field of development in general and evaluation more specifically.

Remote sensing. Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (USGS, 2018). Special cameras and sensors that collect images of the earth remotely may be attached to a variety of platforms such as ships, aircrafts, drones and satellites. Newer, more accurate and higher resolution sensors are being introduced at low or no cost to end users. As an example, the European Space Agency’s Sentinel constellation of satellites promises to provide multispectral images down to a 10 -meter resolution at weekly intervals. In addition, satellite imagery has come to be recognized as a global public good, which has led to satellite images being made available for free to the public at large (Borowitz, 2017). When combined with other methods of collection and analysis, this rich trove of global data can offer evaluators a reliable source for numerous indicators.

## 经济代写|发展经济学代写Development Economics代考|ICT4Eval – The Publication

The discussions in the ICT4Eval conference highlighted the need for further deliberation on specific topics. This book is an endeavour in that regard, to cover further ground on selected topics from the conference. The book is comprised of five chapters and seven individual case studies by 19 authors, who elaborate on experiences of using ICT tools. The first chapter deals with the extensive challenges that lie ahead in assessing progress on SDGs. The second chapter deals with the host of technologies that evaluators could use, followed by examples of how such technologies have been deployed and the results. Contributors to such cases hail from United Nations agencies, governments, non-governmental organizations, private consulting firms, academia and the world of freelance professionals. The third chapter deals with the broader paradigm of big data and how different technologies feed into it. It elaborates on the practical issues that evaluators could face in trying to use existing avenues of big data. The fourth chapter deals with ethics, privacy and biases in using technology for monitoring as well as evaluations. The final chapter deals with the broader implications of technology
for economic development and for countries’ development trajectories.
This book offers a starting point for deliberating on the use of an increasingly complex set of ICT tools in development in general and evaluation in particular. It is the first step in a long iterative process of introducing innovations, learning from them and adapting to changing times. The book has been written by practitioners, for practitioners, to explore ways of harnessing technology for their work, ranging from simple mobile-based tools to cutting-edge neural networks in deep learning and artificial intelligence. It is a book that seeks to demonstrate, by example, the frontiers that can be breached in ICT4Eval. It assimilates a lifetime of experience and work by accomplished practitioners from a wide range of backgrounds. It is an invitation to opening new avenues development challenges of our time.

## 经济代写|发展经济学代写Development Economics代考|ICT4Eval – The Publication

ICT4Eval 会议的讨论强调了对特定主题进行进一步审议的必要性。这本书是在这方面的努力，以涵盖会议中选定主题的进一步基础。这本书由 19 位作者的五个章节和七个单独的案例研究组成，他们详细阐述了使用 ICT 工具的经验。第一章讨论了在评估可持续发展目标进展方面面临的广泛挑战。第二章介绍了评估者可以使用的大量技术，然后是这些技术的部署方式和结果的示例。这些案例的贡献者来自联合国机构、政府、非政府组织、私人咨询公司、学术界和自由职业者的世界。第三章涉及大数据的更广泛范式以及不同的技术如何融入其中。它详细阐述了评估人员在尝试使用现有的大数据途径时可能面临的实际问题。第四章讨论了使用技术进行监测和评估时的道德、隐私和偏见。最后一章讨论技术的更广泛影响

## 有限元方法代写

tatistics-lab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。

## 经济代写|发展经济学代写Development Economics代考|ECONG056

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|García and Prashanth Kotturi

Monitoring and evaluation are essential to gather information from past and current activities that aim to have a positive impact on sustainable development. They are useful to track progress, identify areas for improvement and inform decision-making. The two concepts are not synonymous, however. Evaluation has come a long way since its origin as part of a joint “monitoring and evaluation” process. It is now regarded as an independent function that systematically assesses the achievement of expected and unexpected results. Evaluation is designed to enable judgements about what has worked and not worked, and most importantly to identify the factors leading to performance, while guiding future operations and strategy.

In their most fundamental ways, monitoring and evaluation differ from each other in three significant aspects: timing, purpose and who conducts it. In terms of timing, monitoring takes place throughout the project cycle, while evaluation assesses all or part of the project cycle and is conducted at a certain point in time. Monitoring’s purpose is to track a development intervention’s progress, comparing what is delivered with what is planned. Evaluation, on the other hand, reviews the achievements of a programme and considers whether the programme was the best way to achieve it. It measures both intended and unintended effects of an intervention. More importantly, evaluation attempts to establish some level of attribution of the observed effects to the intervention. Monitoring is typically conducted by third party who can be impartial in consulting with programme staff and stakeholders (CID, 2014).

An evaluator’s role is to investigate and justify the value of an evaluand. Such investigation and justification shall be supported by joining empirical facts and probative reasoning (Scriven, 1986). The responsibilities of evaluators and the expectations of evaluations have increased. Evaluation has evolved from being a self-assessment exercise for development institutions to becoming a tool of impartial scrutiny of operations, reporting usually directly to governing bodies (IOE, 2015b). This evolution requires data collection and analysis to pass a more rigorous methodological test than even that envisaged by Michael Scriven. Different methodologies and methods have comparative advantages in addressing particular concerns and needs.

## 经济代写|发展经济学代写Development Economics代考|The Challenge of Evaluating the Sustainable Development Goals

The SDGs agreed by 193 countries in September 2015, which frame the international development agenda till 2030, benefit from a quarter of a century of evaluating efforts to achieve their predecessors, the Millennium Development Goals (MDGs) (see Chapter 1). At the heart of the SDGs agenda is the need to ensure sustained and inclusive economic growth, social inclusion, and environmental protection, fostering peaceful, just, and inclusive societies through a new global partnership. Hence, the SDGs agenda covers social, economic and, most importantly, environmental sustainability. Such multidimensional nature of sustainable growth requires evaluators to take a systems view of the SDGs. However, taking multidimensionality into account is easier said than done. There are three main challenges for evaluation as pertains to taking a systems view, each flowing from the other. (i) multiple interrelated sectors are involved in achieving SDGs. This brings a greater degree of complexity, which has to be dealt with in new ways. (ii) the complexity of the SDGs also requires multiple levels of interventions (national, regional, local) and the involvement of multiple actors. (iii) in light of the complexity involved, the data and requisite capacities, including national evaluation capacities, are inadequate, so a conceptual framework, and the means therein for evaluating SDGs, are scarce and in some cases do not exist (EvalPartners, 2017). The 17 SDGs are the results of intergovernmental negotiations on priority development challenges facing the world in the 21st century and have 169 targets and 232 indicators. As of September 2017, 145 out of the 232 indicators could not be evaluated due to lack of data or of an internationally agreed methodology (UNDP, 2017).
The advent of the SDGs also brings opportunities. The emerging knowledge in evaluation has led to the recognition of the complexity and interrelatedness of the SDGs and the potential they have to put sustainability at the centre of the sustainable development agenda (Nilsson, 2016). This helps evaluators look at the SDGs as a part of a single system and apply systems thinking, an integrated approach that was largely absent in the process of conceptualizing and evaluating the MDGs. Evaluators cannot tackle the challenges of the SDGs and harness the opportunities that they present using existing paradigms of data collection and analysis. The inherent complexity of the SDGs – and the lack of data for some indicators – implies that evaluators will have to come up with new sources and methods. In a rapidly evolving environment, mustering data of the scale and variety required by the number and diversity of SDG indicators calls for fresh approaches.

## 经济代写|发展经济学代写Development Economics代考|The Challenge of Evaluating the Sustainable Development Goals

2015 年 9 月 193 个国家商定的 SDGs 是到 2030 年的国际发展议程的框架，它受益于 25 年来为实现其前身千年发展目标 (MDGs) 所做的评估工作（见第 1 章）。可持续发展目标议程的核心是需要确保持续和包容性的经济增长、社会包容和环境保护，通过新的全球伙伴关系促进和平、公正和包容的社会。因此，可持续发展目标议程涵盖社会、经济以及最重要的环境可持续性。可持续增长的这种多维性质要求评估者对可持续发展目标采取系统观点。然而，考虑多维性说起来容易做起来难。与采取系统观点有关的评估存在三个主要挑战，彼此流淌。(i) 实现可持续发展目标涉及多个相互关联的部门。这带来了更大程度的复杂性，必须以新的方式处理。(ii) 可持续发展目标的复杂性还需要多层次的干预（国家、区域、地方）和多方参与。(iii) 鉴于所涉及的复杂性，包括国家评估能力在内的数据和必要能力不足，因此评估可持续发展目标的概念框架和其中的手段稀缺，在某些情况下甚至不存在（EvalPartners，2017 ）。17 项可持续发展目标是政府间就 21 世纪世界面临的优先发展挑战进行谈判的结果，共有 169 个具体目标和 232 个指标。截至 2017 年 9 月，

## 有限元方法代写

tatistics-lab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。