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

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我们提供的描述统计学Descriptive statistics及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (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

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.

Decentralized Finance Will Change Your Understanding Of Financial Systems
统计代写|描述统计学代写Descriptive statistics代考|Shifts in Emphasis


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



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


国民核算,作为宏观经济学的一部分,也属于社会经济统计,但在教科书中并未提及,尽管它是整合所有经济统计工作的描述性框架。W. Leontief 的投入产出方案捕捉经济动态,也属于社会经济统计课程。这两个独立的领域属于并且应该在统计学的课程和教科书中进行讨论。区域性的、主要是非实验性的社会经济数据的解释和预测需要重新思考其基础。正如经济和社会现象是任何统计事业的出发点和最终目的地一样,理论也必须从头到尾涵盖整个过程。这个更广泛的理论基础应该涵盖统计描述和统计推断,但是请记住,每项统计工作都需要解释,但不一定是推断。这样一个扩展的理论基础应该能够与认识论、社会学、地理学、经济史、管理科学、会计学、社会伦理学,当然还有经济学分享其关注点。然而,概率计算将不那么突出。伦纳德·J·萨维奇(Leonard J. Savage)所说的只有很少一部分可以用作社会经济统计理论的基础。这样一个扩展的理论基础应该能够与认识论、社会学、地理学、经济史、管理科学、会计学、社会伦理学,当然还有经济学分享其关注点。然而,概率计算将不那么突出。伦纳德·J·萨维奇(Leonard J. Savage)所说的只有很少一部分可以用作社会经济统计理论的基础。这样一个扩展的理论基础应该能够与认识论、社会学、地理学、经济史、管理科学、会计学、社会伦理学,当然还有经济学分享其关注点。然而,概率计算将不那么突出。伦纳德·J·萨维奇(Leonard J. Savage)所说的只有很少一部分可以用作社会经济统计理论的基础。50


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

数据只是与某些“真实价值”的随机偏差的假设是对自然科学中发展的思想的继承。例如,回归图中的数据分散通常被认为是偏离由数学确定的回归线表示的中心。最小二乘回归或趋势线被认为是自然规律的有效近似,可能是化学或物理过程行为的基础。然而,当分解一个社会经济数据集时,子集合中的数据通常具有与它们的集合中的数据不同的参数的回归线。这表明社会上没有与支配物理现象的定律相对应的东西,这一点将在第 1 章中进一步讨论。9.



统计代写|描述统计学代写Descriptive statistics代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。







术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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。



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