### 统计代写|描述统计学代写Descriptive statistics代考|Shifts in Emphasis

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|Shifts in Emphasis

A shift ought to take place, from the frequency distribution approach with the tempting mathematical treatment of numeric characteristics, that prevails in the data of the natural sciences, to the less tractable qualitative and geographic characteristics, the typical determinants of socio-economic data. These, though not as readily convertible to numbers, are the basic features of the data about economic and social phenomena. Returning to its two original functions of capturing and interpreting reality, statistics must deal with distributions by attributes and geographic regions.
The importance of formulating and testing hypotheses about situations in society for managers, business analysts, politicians and lawmakers must be questioned, despite its great interest for research in the natural sciences. Most of the hypotheses formulated in econometrics cannot be legitimately tested in the same way as e.g. hypotheses in the engineering problems of statistical quality control.

The discussion of price measurement needs to be expanded beyond the customary formalistic treatment. Basic issues need to be discussed such as, ‘What is price?’, ‘What is its nature?’, and ‘What is production?’ Price level changes should be discussed as part of time series, not as a separate oddity. The recent, more inclusive social indicators should become part of the wider discussion of economic indicators. All this should become part of a foundation for descriptive socio-economic statistics. 49

## 统计代写|描述统计学代写Descriptive statistics代考|Filling Voids

The classification systems which underlie the aggregates of socio-economic data are rarely discussed in textbooks on socio-economic statistics. They should also become part of statistical theory. The relation between the socio-economic phenomena and the statistical data aggregates will have to be clarified. In the interpretation of time series and in forecasting, such a comprehensive statistical theory must allow for the combination of the quantitative description of these unique, historic and geographic socio-economic situations with the tools of historiography, sociology, philosophy, management, and economics, not with probability theory except in those instances where it is truly warranted.

National accounting, as part of macroeconomics, also belongs in socio-economic statistics, but is not mentioned in textbooks, even though it is the descriptive framework that integrates all statistical efforts regarding the economy. W. Leontief’s input-output scheme, which captures the dynamics of the economy, also belongs in a course on socio-economic statistics. These two separate areas belong and ought to be discussed in courses and textbooks of statistics. The interpretation and prediction of regional, mostly non-experimental socio-economic data requires the re-thinking of their foundation. Just as economic and social phenomena are the point of departure and the final destination of any statistical enterprise, so also must the theory cover that entire process from beginning to end. This much broader theoretical basis should cover both statistical description and statistical inference, keeping in mind, however, that every statistical effort requires interpretation, but not necessarily inference. Such a broadened theoretical foundation should be capable of sharing its concerns with epistemology, sociology, geography, economic history, the science of management, accounting, social ethics, and of course, with economics. The calculus of probability, though, will be less prominent. Only little of what Leonard J. Savage had to say will be of use as a foundation for the theory of socio-economic statistics. ${ }^{50}$
Electronic computers, with their ever-increasing capacity for storing numbers, text and formulas, free statisticians from burdensome sorting and computing, indeed from the drudgery and tedium of what constituted the bulk of their work. This was reflected in the expression ‘Tabellenknechte’ (slaves of tabulations), coined to describe statisticians’ work before the arrival of computers. These should allow statisticians more time to think about the meaning of their results unless they allow the complexities of computer technology to take the place of the drudgery from which they have been recently liberated.

There is also another danger rooted in the ease with which readily available canned statistical procedures and models can be accessed. The F, t, chi- square, and other statistical tests, often routinely and inappropriately applied, can create the illusion that useful, even scientific analysis has been accomplished. Yet, too often the appropriate conditions for using these tests are not given, and fail to help to understand the socio-economic situation. Computers, however, can be very useful in the meaningful interpretation of socio-economic data by aggregation/dis-aggregation, which is discussed in greater depth in subsequent chapters.

## 统计代写|描述统计学代写Descriptive statistics代考|Toward a De-centralized Understanding of Data

The envisioned foundation of descriptive statistics requires a different attitude toward data about business, the economy and society: neither as the highly accurate measurements of natural science phenomena, in which the historic time and geographic place of the measurement is of minor importance, nor as random variables and random samples. On the contrary, in socio-economic data, their location, place in a historic context, and geographic region are of major interest, in realistically portraying these spatial-historical-institutional socio-economic phenomena (to be discussed in the next Chapter). This requires a very different approach to socio-economic statistical data ${ }^{51}$ than the present understanding that treats them as abstract mathematical quantities. As a consequence of this mis-understanding, essential areas have been excluded that really belong to socio-economic statistics.
The assumption that data are only random deviations from some ‘true value’ is a carry-over from the thinking developed in the natural sciences. For example, the scatter of data in a regression diagram is typically considered a deviation from that center represented by the mathematically-determined regression line. The leastsquares regression or trend line is held to be a valid approximation of the natural laws presumably underlying the behavior of chemical or physical processes. When dis-aggregating a socio-economic data set, however, the data in the sub-aggregates usually have regression lines with different parameters than the data in their aggregate. This indicates that there is no counterpart in society to the laws that govern physical phenomena, a matter that is further discussed in Chap. 9 .

American and other societies experience the pull toward greater economic and political autonomy and decentralization, ${ }^{52}$ while at the same time different forces work in the opposite direction, toward greater concentration. The present reduction in the functions and powers of Federal Agencies in the United States are a testimony to this trend toward decentralization The principle of subsidiarity recognizes the greater importance to citizens of what goes on in their immediate neighborhood and in the local district vis-à-vis matters affecting the country or the world as a whole. In statistical data about society an analogous situation should be expected. Averages and other values of centrality and trend values, representing those central values in society, lose their present preponderance that statistics has adopted from the natural sciences. In short, socio-economic data should be recognized as pieces of statistical evidence in their own right, not as deviations from some central value or trend.

This view of socio-economic data as not having a natural, necessary center from which they randomly deviate, is an important feature to be taken into account when interpreting data. This matter is followed-up in the next chapters. ${ }^{53}$ The thinking about socio-economic data ought to shift away from its present belief that they have a center relying on means, trends and the dispersion around them, toward an understanding of socio-economic data as amorphous structures that can be aggregated or de-aggregated by subject categories, regions and time periods, without having such a center.

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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。