统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

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广义线性模型(GLiM,或GLM)是John Nelder和Robert Wedderburn在1972年提出的一种高级统计建模技术。它是一个包括许多其他模型的总称,它允许响应变量y具有正态分布以外的误差分布。

statistics-lab™ 为您的留学生涯保驾护航 在代写Generalized linear model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Generalized linear model代写方面经验极为丰富,各种代写Generalized linear model相关的作业也就用不着说。

我们提供的Generalized linear model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|Generalized linear model代考广义线性模型代写|Levels of Data

统计代写|Generalized linear model代考广义线性模型代写|Defining What to Measure

The first step in measuring quantitative variables is to create an operationalization for them. In Chapter 1, an operationalization was defined as a description of a variable that permits a researcher to collect quantitative data on that variable. For example, an operationalization of “affection” may be the percentage of time that a couple holds hands while sitting next to each other. Strictly speaking, “time holding hands” is not the same as “affection.” But “time holding hands” can be objectively observed and measured; two people measuring “time holding hands” for the same couple would likely produce similar data. However, “affection” is abstract, ambiguous, and unlikely to produce consistent results. Likewise, a researcher could define “attentiveness” as the number of times a parent makes eye contact with a child or the number of times he or she says the child’s name. Again, these operationalizations are not the same thing as “attentiveness,” but they are less ambiguous. More important, they produce numbers that we can then use in statistical analysis.

You may have a problem with operationalizations because using operationalizations means that researchers are not really studying what interests them, such as “affection” or “attentiveness.” Rather, they are studying the operationalizations, which are shallow approximations for the ideas that really interest them. It is understandable why operationalizations are dissatisfying to some: no student declares a major in the social sciences by saying, “lam so excited to learn about the percentage of time that parents hold handsl”
Critics of operationalizations say that – as a result – quantitative research is reductionist, meaning that it reduces phenomena to a shadow of themselves, and that researchers are not really studying the phenomena that interest them. These critics have a point. In a literal sense, one could say that no social scientist has ever studied “affection,” “racism,” “personality, ” “marital satisfaction, ” or many other important phenomena. This shortcoming is not unique to the social sciences. Students and researchers in the physical and biological sciences operationalize concepts like “gravity, ” “animal packs, “and “physical health” in order to find quantifiable ways of measuring them.

There are two responses to these criticisms. First, it is important to keep in mind that quantitative research is all about creating models, which are simplified versions of reality – not reality itself (see Chapter 1). Part of that simplification is creating an operationalization that makes model building possible in the first place. As long as we remember the difference between the model and reality, the simplified, shallow version of reality is not concerning.

My second response is pragmatic (i.e., practical) in nature: operationalizations (and, in turn, models) are simply necessary for quantitative research to happen. In other words, quantitative research “gets the job done,” and operationalizations and models are just necessary parts of the quantitative research process. Scientists in many fields break down the phenomena they study into manageable, measurable parts – which requires operationalization. A philosopher of science may not think that the “because it works” response is satisfactory, but in the day-to-day world of scientific research, it is good enough.

If you are still dissatisfied with my two responses and you see reductionism as an unacceptable aspect of quantitative science, then you should check out qualitative methods, which are much less reductionist than quantitative methods because they focus on the experiences of subjects and the meaning of social science phenomena. Indeed, some social scientists combine qualitative and quantitative research methods in the same study, a methodology called “mixed methods” (see Dellinger \& Leech, 2007). For a deeper discussion of reductionism and other philosophical issues that underlie the social sciences, I suggest the excellent book by Slife and Williams (1995).

统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

Operationalizations are essential for quantitative research, but it is necessary to understand the characteristics of the numerical data that operationalizations produce. To organize these data, most social scientists use a system of organizing data created by Stevens (1946). As a psychophysicist, Stevens was uniquely qualified to create a system that organizes the different types of numerical data

that scientists gather. This is because a psychophysicist studies the way people perceive physical stimuli, such as light, sound, and pressure. In his work Stevens often was in contact with people who worked in the physical sciences – where measurement and data collection are uncomplicated – and the social sciences – where data collection and operationalizations are often confusing and haphazard (Miller, 1975). Stevens and other psychophysicists had noticed that the differences in perceptions that people had of physical stimuli often did not match the physical differences in those stimuli as measured through objective instruments. For example, Stevens (1936) knew that when a person had to judge how much louder one sound was than another, their subjective reports often did not agree with the actual differences in the volume of the two sounds, as measured physically in decibels. As a result, some researchers in the physical sciences claimed that the data that psychologists gathered about their subjects’ perceptions or experiences were invalid. Stevens had difficulty accepting this position because psychologists had a record of success in collecting useful data, especially in studies of sensation, memory, and intelligence.

The argument between researchers in the physical sciences and the social sciences was at an impasse for years. Stevens’s breakthrough insight was in realizing that psychologists and physicists were both collecting data, but that these were different levels of data (also called levels of measurement). In Stevens’s (1946) system, measurement – or data collection – is merely “the assignment of numerals to objects or events according to rules” (p. 677$)$. Stevens also realized that using different “rules” of measurement resulted in different types (or levels) of data. He explained that there were four levels of data, which, when arranged from simplest to most complex, are nominal, ordinal, interval, and ratio data (Stevens, 1946). We will explore definitions and examples of each of these levels of data.

Nominal Data. To create nominal data, it is necessary to classify objects into categories that are mutually exclusive and exhaustive. “Mutually exclusive” means that the categories do not overlap and that each object being measured can belong to only one category. “Exhaustive” means that every object belongs to a category – and there are no leftover objects. Once mutually exclusive and exhaustive categories are created, the researcher assigns a number to each category. Every object in the category receives the same number.

There is no minimum number of the objects that a category must have for nominal data, although it is needlessly complicated to create categories that don’t have any objects in them. On the other hand, sometimes to avoid having a large number of categories containing only one or two objects, some researchers create an “other” or “miscellaneous” category and assign a number to it. This is acceptable as long as the “miscellaneous” category does not overlap with any other category, and all categories together are exhaustive.

统计代写|Generalized linear model代考广义线性模型代写|Other Ways to Classify Data

The Stevens (1946) system is – by far – the most common way to organize quantitative data, but it is not the only possible scheme. Some social scientists also attempt to ascertain whether their data are continuous or discrete. Continuous data are data that permit a wide range of scores that form a constant scale with no gaps at any point along the scale and also have many possible values. Many types of data in the social sciences are continuous, such as intelligence test scores, which in a normal human population range from about 55 to 145 on most tests, with every whole number in between being a possible value for a person.

Continuous data often permit scores that are expressed as fractions or decimals. All three temperature scales that I have discussed in this chapter (i.e., Fahrenheit, Celsius, and Kelvin) are continuous data, and with a sensitive enough thermometer it would be easy to gather temperature data measured at the half-degree or tenth-degree.

The opposite of continuous data are discrete data, which are scores that have a limited range of possible values and do not form a constant, uninterrupted scale of scores. All nominal data are discrete, as are ordinal data that have a limited number of categories or large gaps between groups. A movie rating system where a critic gives every film a $1-, 2-, 3-$, or 4 -star rating would be discrete data because it only has four possible values. Most interval or ratio data, however, are continuous – not discrete – data. The point at which a variable has “too many” values to be discrete and is therefore continuous is often not entirely clear, and whether a particular variable consists of discrete or continuous data is sometimes a subjective judgment. To continue with the movie rating system example, the website Internet Movie Database (IMDb) asks users to rate films on a scale from 1 to 10 . Whether ten categories are enough for the data to be continuous is a matter of argument, and opinions may vary from researcher to researcher.

How to classify and explain data structure types - Quora
统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

广义线性模型代写

统计代写|Generalized linear model代考广义线性模型代写|Defining What to Measure

测量定量变量的第一步是为它们创建操作化。在第 1 章中,操作化被定义为一个变量的描述,它允许研究人员收集关于该变量的定量数据。例如,“感情”的操作化可能是一对夫妇坐在彼此旁边时手牵手的时间百分比。严格来说,“时间牵手”不等于“亲情”。但“牵手的时间”是可以客观观察和衡量的;两个人测量同一对夫妇的“牵手时间”可能会产生相似的数据。然而,“感情”是抽象的、模棱两可的,不太可能产生一致的结果。同样地,研究人员可以将“注意力”定义为父母与孩子进行眼神交流的次数或他或她说出孩子名字的次数。同样,这些操作化与“注意力”不同,但它们不那么模棱两可。更重要的是,它们产生了我们可以在统计分析中使用的数字。

您可能对操作化有疑问,因为使用操作化意味着研究人员并没有真正研究他们感兴趣的东西,例如“感情”或“注意力”。相反,他们正在研究操作化,这是对他们真正感兴趣的想法的肤浅近似。操作化让一些人不满意的原因是可以理解的:没有学生通过说“我很高兴得知父母牵手的时间百分比”来宣布主修社会科学。
操作化的批评者说——因此——定量研究是还原论的,这意味着它将现象简化为自身的影子,并且研究人员并没有真正研究他们感兴趣的现象。这些批评者说得有道理。从字面上看,可以说没有社会科学家研究过“感情”、“种族主义”、“个性”、“婚姻满意度”或许多其他重要现象。这个缺点并不是社会科学所独有的。物理和生物科学的学生和研究人员将“重力”、“动物群落”和“身体健康”等概念操作化,以便找到可量化的测量方法。

对这些批评有两种回应。首先,重要的是要记住,定量研究就是创建模型,模型是现实的简化版本,而不是现实本身(见第 1 章)。这种简化的一部分是创建一个操作化,使模型构建成为可能。只要我们记住模型和现实之间的区别,简化的、肤浅的现实就无关紧要了。

我的第二个回应本质上是务实的(即,实用的):操作化(以及反过来,模型)对于定量研究的发生只是必要的。换句话说,定量研究“完成了工作”,而操作化和模型只是定量研究过程的必要部分。许多领域的科学家将他们研究的现象分解为可管理、可测量的部分——这需要操作化。一位科学哲学家可能认为“因为它有效”的回应并不令人满意,但在日常的科学研究世界中,它已经足够好了。

如果您仍然对我的两个回答不满意,并且您认为还原论是定量科学的一个不可接受的方面,那么您应该检查定性方法,它比定量方法更少还原主义,因为它们关注主题的经验和社会的意义科学现象。事实上,一些社会科学家在同一项研究中结合了定性和定量研究方法,这种方法称为“混合方法”(参见 Dellinger & Leech,2007)。为了更深入地讨论还原论和其他作为社会科学基础的哲学问题,我推荐 Slife 和 Williams (1995) 的优秀著作。

统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

操作化对于定量研究至关重要,但有必要了解操作化产生的数值数据的特征。为了组织这些数据,大多数社会科学家使用 Stevens (1946) 创建的数据组织系统。作为一名心理物理学家,史蒂文斯拥有独特的资格来创建一个组织不同类型的数值数据的系统

科学家们聚集在一起。这是因为心理物理学家研究人们感知物理刺激的方式,例如光、声音和压力。在他的工作中,史蒂文斯经常与从事物理科学(测量和数据收集并不复杂)和社会科学(数据收集和操作化常常令人困惑和杂乱无章)的人接触(Miller,1975)。史蒂文斯和其他心理物理学家注意到,人们对物理刺激的感知差异通常与通过客观仪器测量的这些刺激的物理差异不匹配。例如,Stevens (1936) 知道,当一个人必须判断一种声音比另一种声音大多少时,他们的主观报告往往与两种声音音量的实际差异不一致,以分贝为单位进行物理测量。结果,一些物理科学的研究人员声称,心理学家收集的关于他们受试者的感知或经历的数据是无效的。史蒂文斯很难接受这个立场,因为心理学家在收集有用数据方面有着成功的记录,特别是在感觉、记忆和智力研究方面。

多年来,物理科学和社会科学的研究人员之间的争论陷入僵局。史蒂文斯的突破性见解是意识到心理学家和物理学家都在收集数据,但这些是不同级别的数据(也称为测量级别)。在 Stevens (1946) 的系统中,测量——或数据收集——仅仅是“根据规则将数字分配给对象或事件”(第 677 页)). 史蒂文斯还意识到,使用不同的测量“规则”会产生不同类型(或级别)的数据。他解释说,有四个级别的数据,按照从最简单到最复杂的顺序排列,分别是名义数据、序数数据、区间数据和比率数据(Stevens,1946 年)。我们将探讨每个数据级别的定义和示例。

标称数据。要创建名义数据,有必要将对象分类为互斥且详尽的类别。“互斥”是指类别不重叠,每个被测对象只能属于一个类别。“穷举”意味着每个对象都属于一个类别——并且没有剩余的对象。一旦创建了互斥和详尽的类别,研究人员就会为每个类别分配一个编号。类别中的每个对象都收到相同的编号。

对于名义数据,一个类别必须具有的对象的最小数量没有限制,尽管创建其中没有任何对象的类别是不必要的复杂。另一方面,有时为了避免大量类别只包含一个或两个对象,一些研究人员会创建一个“其他”或“杂项”类别并为其分配一个编号。只要“杂项”类别不与任何其他类别重叠,并且所有类别加在一起是详尽无遗的,这是可以接受的。

统计代写|Generalized linear model代考广义线性模型代写|Other Ways to Classify Data

Stevens (1946) 系统是迄今为止最常见的定量数据组织方式,但它并不是唯一可能的方案。一些社会科学家还试图确定他们的数据是连续的还是离散的。连续数据是允许范围广泛的分数的数据,这些分数形成一个恒定的尺度,在尺度上的任何一点都没有间隙,并且还具有许多可能的值。社会科学中的许多类型的数据是连续的,例如智力测试分数,在大多数测试中,在正常人群中的范围从大约 55 到 145,其中每个整数都是一个人的可能值。

连续数据通常允许以分数或小数表示的分数。我在本章中讨论的所有三个温度标度(即华氏度、摄氏度和开尔文)都是连续数据,使用足够灵敏的温度计,很容易收集在 0.5 度或 10 度处测量的温度数据。

与连续数据相反的是离散数据,它们是具有有限范围的可能值的分数,并且不形成恒定的、不间断的分数尺度。所有名义数据都是离散的,类别数量有限或组间差距较大的有序数据也是如此。影评人给每部电影评分的电影评分系统1−,2−,3−,或 4 星评级将是离散数据,因为它只有四个可能的值。然而,大多数区间或比率数据是连续的——而不是离散的——数据。变量具有“太多”值而不能离散并因此是连续的点通常并不完全清楚,并且特定变量是由离散数据还是由连续数据组成有时是一种主观判断。继续以电影分级系统为例,互联网电影数据库 (IMDb) 网站要求用户按从 1 到 10 的等级对电影进行评分。十个类别是否足以使数据连续是一个争论的问题,并且意见可能因研究人员而异。

统计代写|Generalized linear model代考广义线性模型代写 请认准statistics-lab™

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金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

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)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

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。

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随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

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。

回归分析代写

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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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