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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写概率与统计Probability and 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。我们的专家在代写概率与统计Probability and Statistics方面经验极为丰富，各种代写概率与统计Probability and 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|概率与统计作业代写Probability and Statistics代考|Convenience Sampling

Convenience sampling collects only units from the population that can be easily obtained, such as the top layer of a pallet of boxes or trays with vials or the first cavity in a multi-cavity molding process. This may provide a biased sample, as it represents only one small part or time window of the whole processing window for a batch of products. The term bias indicates that we obtain the value of interest with a systematic mistake: we study bias in more detail later in this chapter. Convenience sampling is often justified by using the argument of population homogeneity. ${ }^8$ This insinuates that either the population units are not truly different or the process produces the population of units in random order. Under these assumptions it is indeed irrelevant which set of units is collected, but these assumptions seem to contradict the need for sampling in the first place and are hardly ever justified.

Haphazard sampling is often believed to be an excellent way of collecting samples, because it gives a feeling or the impression that each unit was collected completely at random. ${ }^9$ This way of sampling is best described by an example. If one stands in a library in front of a bookshelf and one is asked to collect an arbitrary book, then “just picking one” would be a haphazard sample. However, in practice it turns out that this procedure typically collects books in the center of the bookshelf and typically books that are larger or thicker. This is usually not what people feel or believe when they try to take an arbitrary book. Hence despite the feeling of randomness when performing haphazard sampling, often the resulting sample is not truly random. Another example is that human beings have the tendency to choose smaller digits when they are asked to choose digits from 1 to 6 (Towse et al. 2014).

## 统计代写|概率与统计作业代写Probability and Statistics代考|Purposive Sampling

Purposive sampling or judgmental sampling tries to sample units for a specific purpose. This means that the collection of units is focused on one or more particular characteristics and hence it implies that only units that are more alike are sampled. In epidemiological research ${ }^{10}$ purposive sampling can be very practical, since it may be used to exclude subjects with high risks for unrelated diseases. In clinical trials ${ }^{11}$ inclusion (e.g., participants older than 65 years) and exclusion (e.g., no pregnant women) criteria are explicitly applied to make sure a sample has specific characteristics. This way of sampling is strongly related to the definition of the population, since deliberately excluding units from the sample is analogous to limiting the population of interest. Thus purposive sampling may be useful, but it is limited since it does not allow us in general to make statements about the whole population, and at best only about a limited part of the population (although we may not be sure either). In other words, it does most likely produce a biased sample with respect to the complete population.

All the sampling methods discussed above have the risk that some units are much more likely to be included in the sample than others, which can make statistics computed on the sample data bad estimates for the population parameters of interest. Even worse: with non-representative sampling some units are not only more likely to be included in the sample, we also do not actually know how likely units were included. Hence, even if we wanted to, we could not control for these systematic differences between units. When performing representative sampling we sample units in such a way that we do know how likely units are to be included in the sample (even if they will be different from unit to unit).

# 概率与统计作业代考

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## 有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师