统计代写|描述统计学代写Descriptive statistics代考|Misconceptions in Socio-Economic Statistics

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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
Difference between Descriptive and Inferential Statistics - Statistics By  Jim
统计代写|描述统计学代写Descriptive statistics代考|Misconceptions in Socio-Economic Statistics

统计代写|描述统计学代写Descriptive statistics代考|Misconceptions in Socio-Economic Statistics

Statistics is often popularly characterized as measuring and counting. This uncritical transfer of concepts from the natural sciences to the social sciences is misleading. It is important to note that the data* in socio-economic statistics are of a different nature, ${ }^{27}$ to be further discussed in the next chapter. We are not helping matters by referring to the determination of a characteristic as a measurement. Whatever name we assign this process, measuring e.g. the weight of a piece of zinc oxide on an electronic scale, clearly is a different proposition than e.g. determining, through financial accounting, the net value of a business firm for a given time period. Both are referred to as measurements. Yet, the data in the social sciences are not the result of direct observations done by an objective, specially trained outside observer, like for example in microbiology. Most socio-economic statistical data are, in contrast, self-observed, intended to inform about facts that are on a questionnaire or verbally reported to a survey taker, who usually does not do the observation of facts him/herself. These observations properly speaking, are usually carried out by those who are to be observed, as self reporting. ${ }^{28}$ Very few statistical observations relating to economic facts or other aspects of society are made directly by an objective outside observer, like in the daily work of scientists. Instead, the accuracy and veracity of the information depends on the level of education, good will, disposition to cooperate, and the honesty and unfailing memory of the interviewed. This provides an

important difference with the data in the natural sciences. As the subsequent discussion will show, socio-economic data are aggregates that transmit the economic and social reality differently than is generally assumed. One must realize that economic facts, such as e.g. the employment and labor force participation status of persons is not determined or measured with a precision gauge or with an electronic scale.
Nor is counting what it appears to be. The economic entities, e.g. business firms, report their characteristics via questionnaires. It is these questionnaires, not the persons, business firms, etc. that are the things to be counted, as will be discussed in Chap. 2. These questionnaires will be aggregated and provide a stripped down, abstract picture of socio-economic reality, as discussed in Chap. 3. Statistics’ role is that of a reduction lens which condenses phenomena that are too far dispersed to be perceptible. ${ }^{29}$ These economic and social phenomena are too widely scattered, geographically, subject-mater-wise and over time, to be perceptible without the help of statistics. It acts as a macroscope, ${ }^{30}$ an instrument that allows for the perception of things that are too big or too widely scattered to be seen, the opposite of a microscope, which amplifies phenomena that are too small for the unaided eye. The individual cases themselves, represented by questionnaires, are of little interest. It is their distribution over regions, time and subject-matter categories that is the key to interpreting socio-economic phenomena.

统计代写|描述统计学代写Descriptive statistics代考|Symptomatic Omissions

The gaps and omissions found in the textbooks of business and economic statistics reveal the areas that are similarly missing from the theory of socio-economic statistics.

Statistical aggregates are neither discussed nor recognized in their actual geographical historical-institutional context. Population and other economic censuses are hardly ever mentioned in textbooks. Statistical theory rarely contributes to the understanding of categorical or qualitative characteristics. ${ }^{37}$ Yet, these categorical variables that cannot be determined with precision, prevail in socio-economic data, and are more important than the quantitative characteristics in describing socioeconomic reality. Because of its orientation toward measurable, quantitative characteristics of the natural science data, the theory in textbooks of business and economic statistics fails to discuss the important classifications of economic activities SIC and NAICS, of occupations, and of products. Similarly ignored are the important geographic or spatial distributions. Separate specialized treatments of these topics do exist ${ }^{38}$ but are not part of the theoretical foundation of statistics as applied to the social sciences. Nor is there a place for considerations of an international kind at a time when globalization requires the attention of leaders in business, politics and the economy. ${ }^{39}$

Most frequency distributions in socio-economic statistics are decidedly asymmetric. Yet, the orientation toward data in the natural sciences, where symmetrical distributions prevail, has not recognized this. As previously stated, the phenomena in the social sciences differ from those in the natural sciences and these typically highly asymmetric frequency distributions require special treatment with classes of unequal widths, a matter that is rarely mentioned, and whose interpretation, though important, is not on their agenda.

Related is the fact that the statistical perception of reality – disparagingly referred to as only descriptive statistics – is least valued. Publishers of textbooks have advertised, as an improvement in a new edition, that the space allotted to descriptive statistics has been further reduced, in favor of more statistical inference. That misses the point, however, that the original purposes for which business economic and social statistics are produced, is to scan society and its changes, and to report its findings. Statistical methods, most of them transferred from statistics in biology and other natural sciences, hardly take note of economic and social factors and do not present methods to study phenomena such as the extent and intensity of unemployment in different parts of the country, by age, gender, race, occupation, industrial activity, etc. By failing to acknowledge the subject matter-time-space dimensions of social phenomena, statistical theory has turned its back on socio-economic reality, limiting its concerns to concepts of random sample selection, random variable, inference from samples, least squares. and related sample-theoretical considerations. ${ }^{40}$ It is in vogue to construct and study models of reality, rather than to study that reality itself. It is questionable that much can be learned about a situation ${ }^{41}$ through simulation exercises $^{42}$ and testing of hypothetical models.

统计代写|描述统计学代写Descriptive statistics代考|Beyond Sampling and Inference

What should a future theory of business, economic and social statistics contain? Although sampling techniques and the inference from samples are important, socioeconomic statistics literally has been trapped for decades in it as its near-exclusive theory. The situation has not changed with the emergence of non-parametric methods of inference and multivariate analyses. Despite their limited scope, sampling,

inference and decisions based on it are treated as if they were The Theory of Statistics. It was precisely these limited concerns that have kept statistical theorists from returning to the interpretation of the situations described by socio-economic data, which really is the ultimate purpose of statistics. Historically there were similar episodes of the exclusive and limited concern with certain topics. At the turn of the $20^{\text {th }}$ century, for example, discussion centered on the measures of location, dispersion, and index numbers. Neither one of these developments contributed significantly to interpreting socio-economic data

The time has come to break out of the confinement of many decades of exclusive concern with sampling and inference ${ }^{47}$ and to re-orient statistics to interpret the phenomena of society through all kinds of data, not only those from samples. The entire process, from the early draft of the concept of what exactly is to be investigated, to the final presentation and the appropriate storage of results, must be part of a theoretical framework of data interpretation. 48

As statistical aggregates are the instruments through which reality is perceived, these aggregates, the data, ought to be the starting point of all statistical theorizing. Aggregation must be recognized as centrally important. Instead, statisticians have turned to probability to look for answers and by doing so, have further put off the real task of interpreting the situations in society as they are reflected in the data.

Describing Populations and Samples in Doctoral Student Research
统计代写|描述统计学代写Descriptive statistics代考|Misconceptions in Socio-Economic Statistics


统计代写|描述统计学代写Descriptive statistics代考|Misconceptions in Socio-Economic Statistics


也没有计算它看起来是什么。经济实体,例如商业公司,通过问卷调查报告其特征。需要计算的是这些问卷,而不是个人、商业公司等,这将在第 1 章中讨论。2. 这些问卷将被汇总并提供一个精简的、抽象的社会经济现实图景,如第 1 章所述。3. 统计的作用是缩小镜头的作用,它将过于分散而无法察觉的现象浓缩起来。29这些经济和社会现象在地理上、主题方面和随着时间的推移都过于分散,如果没有统计数据的帮助,就无法察觉。它充当宏观镜,30一种允许感知太大或太分散而无法看到的事物的仪器,与显微镜相反,显微镜可以放大肉眼无法看到的现象。以问卷为代表的个别案例本身并没有多大意义。正是它们在地区、时间和主题类别上的分布是解释社会经济现象的关键。

统计代写|描述统计学代写Descriptive statistics代考|Symptomatic Omissions





统计代写|描述统计学代写Descriptive statistics代考|Beyond Sampling and Inference


基于它的推理和决策被视为统计理论。正是这些有限的关注使统计理论家无法回到对社会经济数据所描述的情况的解释上,而这确实是统计学的最终目的。从历史上看,对某些主题的排他性和有限关注也有类似的事件。在轮到20th 世纪,例如,讨论集中在位置、分散和索引数的度量上。这些发展都没有对解释社会经济数据做出重大贡献



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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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