### 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Some Small Data Sets

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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

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Strengths of Cords

Crowder et al. (1991) gave this data set, shown here in Table 1.1, as Example 2.3. The figures are breaking strengths of parachute rigging lines after certain treament. This is of some interest to those too impatient to wait for the aeroplane to land. There are 48 observations, of which the last 7 are right censored, indicated by adding $a+$ sign.

The figures here just form a random sample from some distribution, possibly the least-structured form of data and more common in textbooks than practice. Nevertheless, models can be fitted and assessed and, on the odd occasion, useful inferences made.

Boag (1949) listed the data in his Table II, given in Table 1.2. The groups refer to different types of cancer, different treatments, and different hospitals. There were eight groups, listed as $a$ to $h$ in his Figure 1, but only four appear in the table. The first column gives survival time in months: the data are grouped into six-monthly intervals until three years, after which intervals become wider. In group $e$ the count 232 spans interval 0-12 months, and 156 spans 12-24 months, both indicate by a $+$.

Boag was interested in comparing the fits of lognormal and exponential distributions. He computed expected frequencies to set against those observed, and found that chi-square tests accepted the lognormal and rejected the exponential for groups $a, b$, and $c$ but not $e$ (for which the opposite was obtained).

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Catheter Infection

Collett (2003, Table 4.1) presented some data on 13 kidney dialysis patients, each of whom had a catheter inserted to remove waste products from the blood. The original data were used by McGilchrist and Aisbett (1991) to illustrate regression with frailty. If an infection occurs at the entry site, the catheter has to be removed and the area disinfected. The survival time is the number of days until a first infection occurs; pre-infection removal of the catheter for some other reason produces a right-censored time. Among the other variables recorded were age (years) and sex $(1=$ male, $2=$ female). Collett fitted a Cox proportional hazards model (Section 5.2) and found that sex, but not age, was a significant factor; one can only speculate. He then went on to illustrate the computation and interpretation of various types of residuals.

Table $1.3$ gives some artificial data on 27 patients of the same general type as Collett’s. Here, tim is time and cns is the censoring indicator ( 0 for a right-censored time, 1 for an observed time); observation on each patient was terminated at 28 days, so tim $=28$ entails $c n s=0$. The data will be used for illustration below.

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Inspecting the Data with R

This section and the ones following are for R-novices. If you are among the large number of statisticians more experienced than I am with $R$, go directly to Chapter 2; do not pass GO; do not collect $£ 200$.
I will assume that you have R set up on your computer. Otherwise, and if you do not know how to download it and set it up, phone a friend-I did. If you, like me, grew up in the days before personal computers, when man first stood erect and started to use tools, you will probably need to be guided through abstruse concepts such as working directories. Everitt and Hothorn (2010) tell you how to do it all in plain English that even I can understand. Venables and Ripley (1999) is also highly recommended-when all else fails, read the instructions! (Mrs. Crowder once forced me to stop the car, after driving round in circles for an hour, and ask for directions.)

Incidentally, to perform various data analyses throughout this book, functions have been coded in R; they are available on the Web site referred to in the Preface. There is certainly no claim that they are superior to ones available elsewhere, in the CRAN collection, for instance. But it is often quicker to write your own function than to spend hours searching lists of packages for one that does the job you want. What writing your own code does do, too, is to force you to get to grips with the particular technique better. It also enables you to arrange things as you want them to be arranged. Mainly, it is good practice for tackling data for which there is no off-the-shelf software. How many inappropriate statistical analyses are performed simply because there’s a readily available program that does it?

For illustration let us apply $\mathrm{R}$ to the catheter data (Table 1.3). First, the data should be set up in a plain text file, say catheter 1. dat. The standard format is
id age sex tim ens
1232220
$\begin{array}{lllll}2 & 21 & 1 & 9 & 1\end{array}$
$\begin{array}{llll}27 & 62 & 1 & 10\end{array}$
The data file has a header row at the top, giving names to the columns, and then 27 rows of figures. The file must occupy the current working directory, as defined in your R setup. Now the data must be loaded into $R$ : in the R-window type
dmx=read. table(‘catheter 1, dat’, headerm $)$; attach $(\operatorname{dmx})$; dmx; *input and check data
The option header $=T$ (T means True) indicates that there is a header in the data file (use header $=\mathrm{F}$ if not). The $27 \times 5$ data matrix will now be stored as $\mathrm{dmx}$ : this is created as a list variable. (In a moment of weakness I did once look it up in the manual, which has a whole chapter on lists and data frames, but too long to actually read.) The command attach (dmx) makes the columns accessible for further processing, for example, age is now a numerical vector of length 27. Sometimes you need to force a list to become numeric: this can be done with $d m x=a s$. numeric (unlist $(d m x)$ ). The # symbol indicates a comment: the rest of the line is ignored by the processor. The semicolon separates commands on the same line: some users prefer to have a new line for each command.

Now try some R commands: type the following, one at a time (pressing the Enter key after each), and see what you get:
age; mean(age); avagemean(age); avage; var(age); summary (dmx);
agf=sort(age); agf; agf [1]; hist(age); plot(age,tim); pairs(dmx);
Try variations to see what works and what does not. Incidentally, I just use $=$ in $R$ commands rather than $<-$ because (a) I am more used to it, (b) it is easier to type, and (c) I cannot rid myself of the feeling that $y<-x$ means that $y$ is less than $-x$. If you come across an unfamilar function, such as $y=$ wotsthisdo $(x)$, look it up online by typing help (wotsthisdo). Many more functions can be found in the online manual and in Venables and Ripley (1999).

You will soon decide to type your commands into a text file and just paste them into the $\mathrm{R}$ window: this can save a lot of frustration in retyping to correct minor errors. Throughout this book I will use $\mathrm{R}$ for data processing. My listings of R-code are basic and without frills, reflecting my own level in competence. Aficionados will spot slicker ways of doing things. However, there is a case for transparency, hoping to keep down the number of mistakes. One small tip that might come in handy is as follows: after the customary cursing of the computer for daring to produce errors with your code, close the R-window and start again, sometimes previous assignations can corrupt the current run.

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Strengths of Cords

Boag (1949) 在他的表 II 中列出了数据，如表 1.2 所示。这些组指的是不同类型的癌症、不同的治疗方法和不同的医院。共有八组，列为一种到H在他的图 1 中，但表中只出现了四个。第一列给出了以月为单位的生存时间：数据被分组为每六个月的间隔，直到三年，之后间隔变得更宽。在组中和计数 232 跨越 0-12 个月，而 156 跨越 12-24 个月，两者都由+.

Boag 对比较对数正态分布和指数分布的拟合很感兴趣。他计算了预期频率以与观察到的频率进行对比，并发现卡方检验接受对数正态并拒绝组的指数一种,b， 和C但不是和（获得相反的结果）。

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Catheter Infection

Collett（2003 年，表 4.1）提供了 13 名肾透析患者的一些数据，每位患者都插入了一根导管以清除血液中的废物。McGilchrist 和 Aisbett (1991) 使用原始数据来说明衰弱的回归。如果进入部位发生感染，则必须移除导管并对该区域进行消毒。存活时间是第一次感染发生前的天数；由于某些其他原因在感染前移除导管会产生右删失时间。记录的其他变量包括年龄（岁）和性别(1=男性，2=女性）。Collett 拟合了 Cox 比例风险模型（第 5.2 节），发现性别而非年龄是一个重要因素；只能推测。然后他继续说明各种残差的计算和解释。

## 统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Inspecting the Data with R

id age sex tim ens
1232220
221191
2762110

age; 平均年龄）; avagemean（年龄）；野蛮的；变量（年龄）；摘要（dmx）；
agf=排序（年龄）；agf; agf [1]；历史（年龄）；情节（年龄，蒂姆）；对（dmx）；

## 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。