Posted on Categories:Multilevel Models, 多尺度模型, 数据科学代写, 统计代写, 统计代考

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## 统计代写|多尺度模型代写Multilevel Models代考|Event history and survival models

Modelling the time spent in various states or situations is important in a number of areas. In industry the ‘time to failure’ of components is a key factor in quality control. In medicine, the survival time is a fundamental measurement in studying certain diseases. In economics, the duration of employment periods is of great interest. In education, researchers often study the time students spend on different tasks or activities.
In studying employment histories, any one individual will generally pass through several periods of employment or unemployment, while at the same time changing his characteristics, for example his level of qualifications. From a modelling point of view we need to consider the length of time spent in each type of employment, relating this both to constant factors such as an individual’s social origins or gender, and to changing or time dependent factors such as qualifications and age. In this case the multilevel structure is analogous to that for repeated measures data, with periods taking the place of occasions. Furthermore, generally we would have a further, higher level of the hierarchy, since individuals, which are the level 2 units, are themselves typically clustered into workplaces, which now constitute level 3 units. ${ }^1$ The structure may be even more complicated if these workplaces change from period to period; to include this level in our model, we need to consider cross-classifications of the units (see below). There are particular problems that arise when studying event duration data that are encountered when some information is ‘censored’ in the sense that instead of being able to observe the actual duration we only know that it is longer than some particular value, or in some cases less than a particular value (see Chapter 11).

## 统计代写|多尺度模型代写Multilevel Models代考|Discrete response data

Until now, we have assumed implicitly that our response or dependent variable is continuously distributed; for example, an exam score or anthropometric measure such as height. Many kinds of statistical models, however, deal with categorised responses, in the simplest case with proportions. Thus, we might be interested in a mortality rate, or an examination pass rate and how these vary from area to area or from school to school.

In studying mortality rates in a population, it is often of great concern to try to understand the factors associated with variations from area to area or community to community. This produces a basic 2-level structure with individuals at level 1 and communities at level 2. A typical study might record deaths over a given time period together with the characteristics of the individuals concerned, and level 2 characteristics of the communities, such as their sizes or social compositions. One analysis of interest would be to see whether any of these explanatory variables could explain betweencommunity variation. Another interest might be in studying whether mortality rate differences, say between men and women, varied from community to community.
Such models, part of the class known as generalised linear models, have been available for some time for single level data (McCullagh and Nelder, 1989), with associated software. In Chapter 4 we show how to fit multilevel models with different types of categorical response. Chapter 7 extends this to consider multivariate models with mixtures of different response types.

## 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。