### 统计代写|描述统计学代写Descriptive statistics代考|Structure and Nature of Socio-Economic Data: The Aggregates

statistics-lab™ 为您的留学生涯保驾护航 在代写描述统计学Descriptive statistics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。我们的专家在代写描述统计学Descriptive statistics代写方面经验极为丰富，各种代写描述统计学Descriptive statistics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

## 统计代写|描述统计学代写Descriptive statistics代考|Surveying the ‘Real-Life-Objects’

The process which transforms the ‘real-life-objects’ into ‘statistical-counting-units’ usually is the statistical survey. It can be a census, a sample, or some administrative listing that exists for other purposes but is made available to statistics.

Known is the population census. There are other, less known economic census operations: census of agriculture, of mining, manufacturing, whole-sale-retail establishments, and service industries. Even less known is the US census of governments, in which the local governments in the US are the real-life-objects. Because a census is a costly, major operation that requires a legal basis, a professional staff and big budget allocations, it is carried out only at 5 or 10 year intervals, and the different censuses are scheduled at different times because of the limited administrative capacity of census bureaus.

Another matter are the abundant sample surveys. Unless they are undertaken by a public or private professional sampling organization, they seldom serve a serious statistical purpose, but are used as a pretext to draw attention to a new product or some political cause.

Statistical theory has spent much thought and effort on improving the sample design in selecting the real-life-objects and managing the inevitable (mathematical) sampling error. As already mentioned, sampling theory and inference has dominated the discussion of statistics at the expense of nearly everything else.

This statistical process extracts from the rich reality of the existing ‘real-lifeobjects a simplified – and often distorted – sketch of it on a questionnaire or other means of recording. It is a reduction process that is not reversible: The real-life object, e.g. a human person, cannot be reconstructed from a questionnaire, regardless of how much detail it contains and how conscientiously it has been filled out. Furthermore, once recorded, each ‘statistical-counting-unit’ starts its own existence, separate from, and independent of that of the real-life object. Even if the latter should disappear completely, the ‘statistical-counting-unit’ remains, as a lasting testimony to the former’s existence. When tabulated, it survives even the destruction of the original record, on a questionnaire, punch-card, magnetic tape, CD or other device.

Statistical surveys record the real-life-objects in isolation from their socio-economic context. Usually real-life-objects of one kind are enumerated together, such as the dairy farms located in a country in a census of agriculture. Different types of real-life-objects are surveyed at different times, by different agencies, usually according to different criteria and definitions. No integral census has yet been accomplished that would report together human beings, factories, farms, mines, wholesale and retail establishments, banks and other service establishments, with their relevant characteristics. This inability to survey the entire society and its activities together, at the same time, results in discrepancies and variations in the data that have nothing to do with chance occurrences in the economy, but result from the truncation of socio-economic phenomena through the statistical process

## 统计代写|描述统计学代写Descriptive statistics代考|The ‘Statistical-Counting-Units’

It is interesting to consider the differences between “measurement” in the natural sciences and the corresponding statistical activity in the social sciences. In the natural sciences these measurements are the result of observations by objective especially trained observers, like in the bio sciences, so to speak from the outside of the thing to be measured. In the socio-economic setting the person providing the information e.g. in a population survey, really is the “object” to be observed. That selfreported information from many different informants of varying competence and intelligence is collected by survey takers, who themselves often are insufficiently prepared for that task, acting mostly as mail carriers, not like the observers in the natural sciences. The truthfulness and accuracy of such information depends on the cooperation of these interviewees, a matter that cannot be guaranteed, despite existing laws that require it. Neither their honesty nor the accuracy of their memory can be guaranteed. That is a fundamental, important difference between socio-economic statistical data and the measurement data in the natural sciences.

Statistical data have been variously classified. The distinction in ‘Punkt- and Streckenmassen’15 (point- and line masses), for example, is based on the length of life of the real-life-objects: some real-life-objects are perceived as being points in time, of short duration. Others last long, occupying a ‘Strecke’ that is, a considerable stretch of time. But every real-life-object has a certain duration. Considering its life span as point-like and short, or as long lasting, is a relative matter. Moreover, this distinction ignores the fact, that we do not deal with the real-life-objects themselves but with the ‘statistical-counting-units’ which are, by their nature, points in time and space, regardless of the length of life of the real-life object.

Another distinction in ‘Bestands- and Bewegungsmassen’ – inventories of a mass of stationary real-life-objects and masses of moving real-life-objects that are not stationary – is based on the spurious distinction between existence-units which are real-life objects that remain in their location without moving, and motion-units, that is, real-life objects that are on the move, without a fixed relation to a place in a geographic region. That obscures the fact, that every ‘statistical-counting-unit’ is a static record, fixed in a certain time and location, regardless of whether a real-lifeobject is static or dynamic. ${ }^{16}$

A distinction could be made between different types of ‘statistical-countingunits’ according to the occasion of their registration:

1. Real-life-objects are contacted by mail, telephone or personal visit by a concerted effort to record them, and approached at a certain point in time as in a census or sample survey, or
2. A government or private institution records the real-life object on the occasion of some event that triggers a registration, such as a beginning of something, a change of its characteristics, or its termination, carried out for other than statistical purposes. Typical is the registration of the birth of a child, the issue of a building permit for an addition to an existing building or for a new building, the registration of the bankruptcy of a firm (death), or the periodic re-registration of motor vehicles. In most of these instances the registration is requested by law,is carried out as a continuing operation, often for the purpose of taxation, not originally for statistical purposes.

## 统计代写|描述统计学代写Descriptive statistics代考|The Tri-Dimensional Frame of an Aggregate

Socio-economic phenomena deal not only with a subject-matter aspect but also with a time and a regional-geographic aspect. The real-life-objects, and their corresponding ‘statistical-counting-units’ that portray those phenomena, partake in those three aspects that can be conveniently visualized as the three perpendicular vectors or dimensions of a coordinate system. This means that every aggregate ${ }^{1}$ that deals with socio-economic phenomena can be understood as occupying a tri-dimensional space like in a Cartesian coordinate system (Fig. 3.1).

The subject-matter dimension can be presented on the vertical vector of a statistical aggregate, or on any other of the vectors, if so preferred. The sub-divisions of the subject-matter, e.g. the major groupings of the classification of economic activities, can be indicated in linear form by corresponding tick-marks. ${ }^{2}$

For the social sciences the development of phenomena over time is of great interest. Time, therefore, should be marked on the second vector of that – still empty – Cartesian space, facing the observer, using the customary subdivisions of the calendar (months, quarters, semesters).

On the third, geographic vector, the administrative regions are plotted as a onedimensional sequence. Regional districts, reduced to linear form, are projected on the geographic vector. Figure $3.2$ shows the tri-dimensional frame of a statistical aggregate before the ‘statistical-counting-units’ are placed into it. One could imagine this empty space framed by the three vectors to look like an empty fish tank with its three dimensions.

It is important to recognize that these three dimensions are present in all statistical data. This is easily overlooked, because data published in tabular form usually present only two of these three dimensions, either the subject-matter and time, or the subject-matter and geographic-territorial-dimension. This is true as much for aggregates as it is for other data-materials that are derived from aggregates.

When the time dimension is small, like in a census or inventory, the tri-dimensional character of a statistical aggregate shrinks to a seemingly two-dimensional sheet and is easily overlooked. Yet, like a sheet of paper that, regardless how thin it may be, still has a thickness that becomes evident when e.g. 500 sheets of such thin papers are packaged as a ream. In the case of a survey, the time dimension of the resulting data consists of those few hours or days – in a census of a big country that may be many months – needed to accomplish the field work, capturing a specific socio-economic phenomenon at that particular point in time. It may take that long to locate the respective ‘real-life-objects,’ to interview or canvass them and forward the result to a central location, an office, to produce the ‘statistical-counting-units’. The placement of the resulting aggregates on the time vector, Fig. 3.3, is important, because it allows to connect them to other statistical and non-statistical materials. ${ }^{3}$

## 统计代写|描述统计学代写Descriptive statistics代考|The ‘Statistical-Counting-Units’

“Bestands-and Bewegungsmassen”中的另一个区别——大量静止的现实生活对象和大量非静止的移动现实生活对象的清单——是基于现实生活中的存在单位之间的虚假区别保持在其位置而不移动的对象，以及运动单元，即移动中的真实对象，与地理区域中的位置没有固定关系。这掩盖了这样一个事实，即每个“统计计数单元”都是静态记录，固定在某个时间和地点，无论现实生活中的对象是静态的还是动态的。16

1. 现实生活中的对象通过邮件、电话或个人访问进行联系，共同努力记录它们，并在某个时间点（如在人口普查或抽样调查中）接近，或
2. 政府或私人机构在某些触发注册的事件之际记录现实生活中的对象，例如出于统计目的以外的目的进行的某事的开始、其特征的变化或终止。典型的例子是孩子的出生登记、为现有建筑物增建或新建建筑物的建筑许可证的签发、公司破产（死亡）的登记或汽车的定期重新登记汽车。在大多数情况下，注册是法律要求的，是作为持续经营进行的，通常是出于税收目的，而不是最初用于统计目的。

## 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。