统计代写|描述统计学代写Descriptive statistics代考|Location, Extension and Mobility of ‘Real-Life-Objects’

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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|Location, Extension and Mobility of ‘Real-Life-Objects’

统计代写|描述统计学代写Descriptive statistics代考|Location, Extension and Mobility of ‘Real-Life-Objects’

Every ‘real-life-object’ has a definite relation to its location. Reference to it as the ‘geographic characteristic’ treats location as an intrinsic quality of an object, at a par with other characteristics. This assessment is inaccurate, however, and prevented statistical theory from dealing with the geographic dimension of socio-economic phenomena. Regional phenomena differ due to the special economic and environmental characteristics of each area, which are implied and summarily stated through a ‘real-life-objects’ geographic location. Even ‘real-life-objects’ with only a symbolic, minimal physical substance like the sale of a car or the issuance of a mortgage happen in a place on the map. The geographical location on which a sale takes place, though not an attribute of the ‘real-life-object’ ‘sale’ is, like the time at which it happened, important for grouping these objects into meaningful aggregates (more in Chap. 3).

Every object also has a geographic extension. A farm occupies a certain amount of land with certain surface and soil characteristics. So does a strike which takes place in some production plant. The plant’s physical and geographic extension is usually also the geographic extension of that ‘strike.’

Objects can be fixed or mobile with regard to their location. Most ‘real-life-objects’ are neither absolutely fixed, nor completely mobile. Even houses and large firms have been moved to different locations. It is the high mobility of some ‘real-life-objects’ that creates problems for statistics. Examples are the whereabouts of the rolling stock of a trucking firm or of a railroad company. These problems create uncertainty, not unlike the measuring problems in atomic physics.

统计代写|描述统计学代写Descriptive statistics代考|Attributes and Variables

These ‘real-life-objects’ project an economic phenomenon through their properties. The attributes – qualitative characteristics or non-measurable variables – of these real-life-objects describe pervasive, essential aspects of an object, through non-numeric, nominal description. They cannot be determined with accuracy or measured on an interval or ratio scale. Quantitative characteristics, on the other hand, expressing intensity or the magnitude of some feature, can be determined accurately, but contribute little to characterize the object. ${ }^{13}$ Both kinds of determining the characteristics of a ‘real-life-object’ are needed as mutual complements. ${ }^{14}$
Every property which characterizes a ‘real-life-object’ may be understood as a partial description of its nature. Behind the customary distinction in qualitative characteristics (attributes) and quantitative characteristics (variables) really is another distinction, according to the width of the segment of the integral nature of the ‘reallife-object’ which is provided by a given characteristic. Qualitative characteristics capture in literary form essential and pervasive aspects of the ‘real-life-object,’ but cannot be determined succinctly. The wider that slice out of the nature of a ‘reallife-object’, a specific attribute, the less precisely can it be determined. The so-called quantitative characteristics, on the other hand, refer to narrow segments of the nature of the ‘real-life-object’ which can be determined more accurately. The narrower this segment, the more precisely it can be captured (measured), but the less information is obtained concerning that ‘real-life-object’.

As a first approximation, a wide part of the nature of a ‘real-life-object’ is described through a qualitative characteristic. In consecutive, progressively finer determinations (descriptions) the nature of that initial segment of the ‘real-lifeobject’ is then further defined. At the end of such a wedge-like penetration into the nature of the ‘real-life-object’, quantitative, measurable characteristics can add the sharpness that was missing in the initial description by the attributes. The same holds for the tabulations made of such characteristics of the ‘real-life-objects.’

When the ‘real-life-object’ is an occurrence, it is also characterized by the ‘reallife-object’ to which it belongs, or on which it is happening. The characteristics of non-individualized ‘real-life-objects,’ e.g. raw materials, are summarily estimated. From the socio-economic point of view they usually are of little interest – although they may be of interest e.g. from a quality-control, that is, engineering point-of-view.
To summarize, the qualitative description alone is imprecise, e.g. a firm described only by the nature of its products. The quantitative description alone has little meaning, e.g. a firm described only by the number of its employees, or the size of last month’ sales, without an indication of its qualitative characteristics like the industry to which it belongs, the kind of products, form of ownership, capital structure, etc. The description of a ‘real-life-object’ by attributes does not need to be supplemented by quantitative characteristics – measurements – in order to be comprehensible.

统计代写|描述统计学代写Descriptive statistics代考|From ‘Real-Life-Object’ to ‘Statistical-Counting-Unit’

The printed socio-economic data do not directly deal with the ‘real-life-objects’ that were discussed, but with simplified statistical sketches of these, that I would like to call the ‘statistical-counting-units.’ It is these that are tabulated, not the ‘real-life-objects’ themselves. The user of statistical data knows only about those ‘real-life-objects’ of which questionnaires or computer accessible evidence – the ‘statistical-counting-units’ – exist. A clear distinction must be made between the ‘real-life-objects’ out there in reality, and the ‘statistical-counting-units, the sketches of these ‘real-life-objects’ in electronic or in other storable form. That seemingly subtle distinction, however, is important and must be kept in mind when interpreting socio-economic data (Fig. 2.1).

统计代写|描述统计学代写Descriptive statistics代考|Location, Extension and Mobility of ‘Real-Life-Objects’


统计代写|描述统计学代写Descriptive statistics代考|Location, Extension and Mobility of ‘Real-Life-Objects’




统计代写|描述统计学代写Descriptive statistics代考|Attributes and Variables




统计代写|描述统计学代写Descriptive statistics代考|From ‘Real-Life-Object’ to ‘Statistical-Counting-Unit’

印刷的社会经​​济数据不直接涉及所讨论的“现实生活对象”,而是用这些简化的统计草图,我想称之为“统计计数单位”。列表中的是这些,而不是“现实生活中的对象”本身。统计数据的用户只知道存在问卷或计算机可访问证据的那些“现实生活对象”——“统计计数单位”。必须在现实中的“现实生活对象”和“统计计数单位”之间做出明确的区分,这些“现实生活对象”的电子或其他可存储形式的草图。然而,这种看似微妙的区别很重要,在解释社会经济数据时必须牢记(图 2.1)。

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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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