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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|Descriptive vs. Inferential Statistics

The first question we need to consider before the long journey to explore the theory of probability is:
Why do we need probability theory?
The brief answer is that it frames both the foundation and the relevant inference procedures for empirical modeling. What distinguishes statistical inference proper from descriptive statistics is the fact that the former is grounded in probability theory. In descriptive statistics one aims to summarize and bring out the important features of a particular data set in a readily comprehensible form. This usually involves the presentation of the data in tables, graphs, charts, and histograms, as well as the computation of summary “statistics,” such as measures of central tendency and dispersion. Descriptive statistics, however, has one very crucial limitation: conclusions from the data description cannot be extended beyond the data in hand.

A serious problem during the early twentieth century was that statisticians would use descriptive summaries of the data, and then proceed to claim generality for their inferences beyond the data in hand.

The conventional wisdom at the time is summarized by Mills (1924), who distinguishes between “statistical description” and “statistical induction,” where the former is always valid and “may be used to perfect confidence, as accurate descriptions of the given characteristics”

## 统计代写|统计推断代写Statistical inference代考|The Basic Structure of a Simple Statistical Model

The simple statistical model, pioneered by Fisher (1922a), has two components:
[i] Probability model $\Phi=\left{f(x ; \theta), \theta \in \Theta, x \in \mathbb{R}{X}\right}$; [ii] Sampling model $\mathbf{X}:=\left(X{1}, X_{2}, \ldots, X_{n}\right)$ is a random sample.
The probability model specifies a family of densities $(f(x ; \theta), \theta \in \Theta)$, defined over the range of values $\left(\mathbb{R}_{X}\right)$ of the random variable $X$, one density function for each value of the parameter $\theta$, as the latter varies over its range of values $\Theta$ : the parameter space (hence the term parametric statistical model).

Example 2.2 The best way to visualize a probability model is in terms of Figure 2.3. This diagram represents several members of a particular family of densities known as the two-parameter gamma family and takes the explicit form
$$\Phi=\left{f(x ; \boldsymbol{\theta})=\frac{\beta^{-1}}{\Gamma[\alpha]}\left(\frac{x}{\beta}\right)^{\alpha-1} \exp \left{-\left(\frac{x}{\beta}\right)\right}, \boldsymbol{\theta}:=(\alpha, \beta) \in \mathbb{R}{+}^{2}, x \in \mathbb{R}{+}\right}$$
where $\Gamma[\alpha]$ denotes the gamma function $\Gamma[\alpha]=\int_{0}^{\infty} \exp (-u) \cdot u^{\alpha-1} d u$.
NOTE: The particular formula is of no intrinsic interest at this stage. What is important for the discussion in this section is to use this example in order to get some idea as to what lies behind the various symbols used in the generic case. For instance, the parameter space $\Theta$ and the range of values $\mathbb{R}{X}$ of the random variable $X$ are the positive real line $\mathbb{R}{+}:=(0, \infty)$, i.e. $\Theta:=\mathbb{R}{+}$and $\mathbb{R}{X}:=\mathbb{R}_{+}$. Each curve in Figure $2.3$ represents the graph of one density function (varying over a subset of the range of values of the random variable $X$ .

# 统计推断代考

## 统计代写|统计推断代写Statistical inference代考|Descriptive vs. Inferential Statistics

20 世纪初的一个严重问题是统计学家会使用数据的描述性摘要，然后继续声称他们的推论具有普遍性，而不是手头的数据。

Mills (1924) 总结了当时的传统智慧，他区分了“统计描述”和“统计归纳”，前者总是有效的，并且“可以用来完善置信度，作为对给定特征的准确描述”

## 统计代写|统计推断代写Statistical inference代考|The Basic Structure of a Simple Statistical Model

[i] 概率模型 \left 的分隔符缺失或无法识别 $\quad$; [ii] 抽样模型 $\mathbf{X}:=\left(X 1, X_{2}, \ldots, X_{n}\right)$ 是一个随机样本。

## 有限元方法代写

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## 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