assignmentutor™您的专属作业导师

assignmentutor-lab™ 为您的留学生涯保驾护航 在代写统计推断Statistical inference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。我们的专家在代写统计推断Statistical inference代写方面经验极为丰富，各种代写统计推断Statistical inference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|统计推断代写Statistical inference代考|A Statistical Model with a Random Sample

What makes the generic statistical model specified in Section $2.2$ simple is the form of the sampling fordel, the random sample assumption. This assufnption involves two intefrelated notions known as independence and identical distribution. These notions can be explained intuitively as a prelude to the more formal discussion that follows.

Independence. The random variables $\left(X_{1}, X_{2}, \ldots, X_{n}\right)$ are said to be independent if the occurrence of any one, say $X_{i}$, does not influence and is not influenced by the occurrence of any other random variable in the set, say $X_{j}$, for $i \neq j, i, j=1,2, \ldots, n$.

Identical distribution. The independent random variables $\left(X_{1}, X_{2}, \ldots, X_{n}\right)$ are said to be identically distributed if their density functions are identical in the sense that
$$f\left(x_{1} ; \theta\right)=f\left(x_{2} ; \theta\right)=\cdots=f\left(x_{n} ; \theta\right) .$$
For observational data the validity of the IID assumptions can often be assessed using a battery of graphical techniques, as discussed in Chapters 5 and $6 .$

## 统计代写|统计推断代写Statistical inference代考|Outlining the Early Milestones of Probability Theory

In an attempt to give the reader some idea as to the origins and the development of probability theory, we present the milestones over the last four centuries; for a more detailed account, see Stigler (1986), Porter (1986), Hacking (2006), Hald (1990), Maistrov (1974), and Gorroochurn (2012).

Glimpses of probabilistic ideas relating to the odds of winning or losing in dice and card games can be traced back to Gerolamo Cardano (1501-1576) in his book “The Book on Dice Games,” published posthumously in 1663. Cardano calculated the odds in dice and card games in the context of discussing fair bets and introduced the idea of the number of equally possible outcomes and the proportion relating to an event. Apart from certain isolated instances of combinatorial calculations, nothing very significant happened for the next century or so, until the well-known series of letters between Pierre de Fermat (16011665 ) and Blaise Pascal (1623-1662) in relation to probabilities associated with games of chance. The origins of probability theory as providing systematic ways for solving problems in games of chance appeared in these letters. Pascal and Fermat are credited with the first correct answer to an old problem of dividing the stakes when a fair game is stopped before either player wins. The next important milestone was the book “How to Reason in Dice Games” by Christiaan Huyghens (1629-1695), which proved to be the first widely read textbook on probability pertaining to games of chance. Huyghens introduced the fundamental notion of mathematical expectation and the basic rules of addition and multiplication of probabilities. The next influential book on probability, entitled “The Art of Conjecturing,” was written by James Bernoulli (1654-1705) and published posthumously in 1713 by his nephew Nicholas. This was a turning point for probability theory because it went beyond the probabilities associated with games of chance and proved the first of the socalled limit theorems, known today as the Law of Large Numbers, as a justification for using observed frequencies as probabilities. This thread was taken up by Abraham de Moivre (1667-1754), who proved the second limit theorem, known today as the Central Limit Theorem, in his book “The Doctrine of Chances,” published in 1718. Important notions such as independence and conditional probabilities were formalized for the first time by de Moivre.

# 统计推断代考

## 统计代写|统计推断代写Statistical inference代考|A Statistical Model with a Random Sample

$$f\left(x_{1} ; \theta\right)=f\left(x_{2} ; \theta\right)=\cdots=f\left(x_{n} ; \theta\right) .$$

## 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