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

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|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.

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## MATLAB代写

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