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• 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

## 统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Instrumentation and Interviewer Training

Finally, other studies exemplified the use of machine learning with a focus on instrumentation, interface development, and interviewer training. Arunachalam et al. (2015) showed how MCs and artificial neural networks (ANNs) can be used to improve computer-assisted telephone interviewing for the American Time Use Survey. Using algorithms that are particularly useful for temporal pattern recognition in combination with paradata, the authors’ goal was to predict a respondent’s next likely activity. This next likely activity would then be displayed live for interviewers on their computer assisted telephone interviewing (CATI)-screens based on time of day and previous activity to facilitate probing and data entry reducing item nonresponse. This process should ultimately improve data quality and increase data collection efficiencies. Although both algorithms predicted the respondents’ activity sequence accurately, the authors found a higher predictive accuracy for the ANNs. Machine learning has also been used to improve the survey instrument for open-ended questions, such as questions regarding occupation (see Section 1.6.1). To facilitate respondent retrieval, decrease respondent burden, and reduce coding errors, Schierholz et al. (2018) investigated computer-assisted coding. More specifically, they assessed the performance of matching algorithms in combination with gradient-boosting decision trees, suggesting a potential occupation based on a verbatim response initially provided by the respondent. Respondents then selected their occupation authors showed that the algorithm detected possible categories for $90 \%$ of all respondents, of which $80 \%$ selected a job title and 15\% selected “different occupation” thereby significantly reducing the resources needed for postinterview coding. Other applications of machine learning algorithms, such as regularization networks, test-time feature acquisition, or natural language processing can be used to reduce respondent burden and data collection cost by informing adaptive questionnaire designs (e.g. for nonresponse conversion) in which individuals receive a tailored number or order of questions or question modules, tailored instructions, or particular interventions in real time, depending on responses to earlier questions and paradata (e.g. for surveys more generally, Early 2017; Morrison et al. 2017; Kelly and Doriot 2017; for vignette surveys or conjoint analysis in marketing, Abernethy et al. 2007; for intelligent, dialogue-based or conversational, tutoring systems, or knowledge assessments, Niraula and Rus 2014).

## 统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Alternative Data Sources

Other areas of application of MLMs for questionnaire design include the collection or extraction and processing of data from alternative (Big) Data sources. For example, collecting data from images (e.g. expenditure data from grocery or medical receipts, Jäckle et al. 2019), or websites and apps such as Flickr, Facebook, Instagram or Snapchat (Agarwal et al. 2011), or sensors (e.g. smartphone sensors capturing geo-location and app use, fitness trackers, or eye trackers) may allow researchers to simplify the survey questionnaire and reduce the data collection burden for respondents by dropping some questions entirely. Processing these Big Data, is however, often impossible with standard techniques and requires the use of MLMs to extract features. Among these are deep learning for image processing (e.g. student transcripts, ${ }^3$ photos of meals or receipts to keep food logs in surveys about food or health, ${ }^4$ or aerial images to assess neighborhood safety) (Krizhevsky, Sutskever, and Hinton 2012); natural language processing (e.g. to understand spoken meal descriptions (Korpusik et al. 2016); code student transcripts (Shorey et al. 2018)), or the use of Naïve Bayesian classifiers or density-based spatial clustering algorithms (e.g. applied to high-dimensional sensor data from smartphones to optimize the content, frequency, and timing of intervention notifications (e.g. Morrison et al. 2017), to detect home location (e.g. Vanhoof et al. 2018), or to investigate the relationship between location data and an individual’s behavior such as exercise frequency (e.g. Eckman et al. 2019)).

# 数据科学、大数据和数据多样性代考

## 统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Alternative Data Sources

MLM 在问卷设计中的其他应用领域包括从替代（大）数据源收集或提取和处理数据。例如，从图像（例如，来自杂货店或医疗收据的支出数据，Jäckle 等人，2019 年）或网站和应用程序（例如 Flickr、Facebook、Instagram 或 Snapchat（Agarwal 等人，2011 年））或传感器（例如智能手机）中收集数据捕获地理位置和应用程序使用的传感器、健身追踪器或眼动追踪器）可以让研究人员通过完全放弃一些问题来简化调查问卷并减轻受访者的数据收集负担。然而，使用标准技术处理这些大数据通常是不可能的，并且需要使用 MLM 来提取特征。其中包括用于图像处理的深度学习（例如学生成绩单、3膳食或收据的照片，用于在有关食品或健康的调查中保存食品日志，4或航拍图像来评估社区安全）（Krizhevsky、Sutskever 和 Hinton 2012）；自然语言处理（例如理解口语膳食描述（Korpusik et al. 2016）；编码学生成绩单（Shorey et al. 2018）），或使用朴素贝叶斯分类器或基于密度的空间聚类算法（例如应用于高来自智能手机的三维传感器数据，以优化干预通知的内容、频率和时间（例如 Morrison 等人 2017），检测家庭位置（例如 Vanhoof 等人 2018），或调查位置数据与个人信息之间的关系运动频率等行为（例如 Eckman et al. 2019））。

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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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师