## 统计代写|统计推断代写Statistical inference代考|STAT3923

2023年3月23日

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• 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
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## 统计代写|统计推断代写Statistical inference代考|The Unfolding Story Ahead

In order to enhance our understanding of the concept of a simple statistical model, we will relate the probabilistic concepts making up this model to real data. The link between the probabilistic assumptions and plots of real data is the subject matter of Chapter 5 . The main objective of Chapters 6-8 is to extend the domain of applicability of the simple statistical model in order to enable one to model more realistic observable phenomena of interest.
Important Concepts
Random vector, bivariate and multivariate distributions, joint density function, bivariate exponential and Normal distributions, joint moments, covariance, skewness and kurtosis coefficients for bivariate distributions, marginal distributions, conditional distributions, conditional moments (raw and central), truncation, hazard function, independence among random variables, identical distributions for random variables, functions of random variables, distributions of functions of random variables, ordered sample, distributions of ordered statistics, simple (generic) statistical model, simple Bernoulli model, simple Normal model, statistical identification of parameters, parameterization, reparameterization.
Crucial Distinctions
Discrete vs. continuous random vectors, conditional probability vs. conditional distributions, marginalization vs. conditioning, conditioning on events vs. conditioning on random variables, marginal vs. conditional moments, sampling space vs. sampling model, statistical vs. substantive parameterizations, statistical vs. structural identification.

## 统计代写|统计推断代写Statistical inference代考|Early Developments in Graphical Techniques

Descriptive statistics can be traced back to John Graunt (1662) and William Petty (1690), but the systematic use of graphical techniques in descriptive statistics dates back to William Playfair (1786, 1801), who introduced bar diagrams, pie charts, and line graphs. A few years later, Fourier introduced the cumulative frequency polygon and in the mid-nineteenth century, Quetelet (1849) introduced the widely used diagrams known as the histogram and its sister, the frequency polygon.

Karl Pearson was an advocate of analyzing data graphically and coined most of the terminology in use today, including that of the histogram, utilizing mostly Greek words (see Pearson, 1892). Histogram is a compound of two Greek words, $\iota \sigma \tau \dot{o} \zeta$ (pole) and $\gamma \rho \alpha \mu \mu \dot{\eta}$ (line). Polygon is also a compound Greek word, made up of the words $\pi o \lambda \dot{v}$ (many) and $\gamma \omega \nu i \alpha$ (angle).

The modern era of graphical analysis in empirical modeling can be dated back to Tukey (1962), but a key paper which revived interest in graphical techniques is arguably Anscombe (1973), by demonstrating the dangers of relying (exclusively) on numerical results when modeling in the context of the linear regression model; see Chapter 14. A good summary of the graphical techniques as of the early 1980s is given in Tukey (1977) and Cleveland (1985, 1993).

# 统计推断代考

## 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。