statistics-lab™ 为您的留学生涯保驾护航 在代写应用统计applied 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。我们的专家在代写应用统计applied statistics代写方面经验极为丰富，各种代写应用统计applied 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

Spreadsheets (MS Excel, Google Sheets, etc.) are very useful for entering data, but not necessarily for analyzing data. Their flexible nature enables the user to enter all sorts of different pages with a variety of notes and a way to store those data. You can do things like color code individual cells or columns. In my experience, people are often terrible at organizing their data in a way that makes it useful for analysis. Most of the time, folks view their spreadsheets as a simple place to dump information. Your Excel file is not your scrapbook! For example, look at Figure 1.3.

On one level, this might seem like an intuitive way to enter our data. We can clearly see that Susan measured the animals in Block 1 and Darren measured the animals in Block 2. We can see that each person measured two tanks per block, and they measured 4 animals in each tank. Great right? But if you look closer, you can see that Susan called the four animals in each tank the same things (tadpoles 1-4), whereas Darren gave them unique IDs (tadpoles 1-8). Susan used lowercase letters to abbreviate snout-vent length

(SVL) for tank 1 but capitalized it for tank 2. Darren forgot to include a space in between “Tadpole” and “6.” $R$ will treat typos like these as separate and independent, causing problems. Evidently Susan didn’t measure the tails in tank 2 at all and just left the cells blank. Darren had two tadpoles without measurable tails and wrote the word “none” in each cell. All of these sorts of things would make the data impossible to analyze.

A much better way to organize these data is shown in Figure 1.4. What you want to aim for is one observation per row, which places data into a relatively long format. In this version, you have a separate column for each type of measurement you have taken and a separate row for each individual that has been measured. Each individual is given a unique identifier. NAs are used in place of any missing data. It might seem weird to have things repeated on many lines, such as the name of the measurer (Susan vs Darren) but you want each row to have all the information necessary to identify it.

## 统计代写|应用统计代写applied statistics代考|UNDERSTANDING VARIOUS TYPES OF OBJECTS IN R

There are a number of different types of objects in $R$, and it is important to understand how each of these work (Table 1.1). There are certainly more types of objects than these, but these are the foundational objects you need to understand for now. For each of these types of objects (and most anything in R), we can ask $R$ what kind of object it is by using the str() function (short for “structure”).
1.9.1 Vector
A vector has only a single dimension, it is a sequence of elements that are all the same type. The length of the vector is defined by the number of elements in the vector. All of these must be the same mode (hopefully you remember what the mode is from just a few pages ago!).
$\operatorname{str}(r 1)$

###### num $[1: 100] \quad 6.87 \quad 4.38 \quad 4.38 \quad 6.58 \quad 8.62 \ldots$

The structure of object $\mathbf{r l}$ tells us it is a numeric vector with 100 elements, and it gives us the first five elements. We can also ask R directly if $\mathbf{r l}$ is a vector.

1. vector $(r 1)$

## 统计代写|应用统计代写applied statistics代考|Matrix

A matrix is essentially a vector that has been given an additional attribute, which is just where to wrap around to create multiple rows or columns. Thus, it’s a vector that has a 2-dimensional structure. Since it is basically just a fancy vector, all the elements in a matrix still need to be of the same mode (e.g. “numeric,” “logical,” etc.).

To create a matrix, we can specify the data to start off with, plus the number of rows and columns and if the data should be wrapped based on rows or columns (with the “byrow=” argument). Note that there is a “bycol $=$ ” argument which does the opposite of “byrow=.” Also note that by saying you don’t want to wrap by row (“byrow=FALSE”) you are doing the exact same thing as saying “bycol=TRUE.”

A data frame is probably the most useful and most used of the objects we will discuss in this book. I know I said earlier that vectors are the most important, and they are, but a data frame is essentially a table composed of one or more vectors. Thus, if you can understand vectors you can understand data frames. All of the vectors in a data frame have to have the same length (which is important), but the data in those vectors can be different modes (which is also important). We will learn how to read in data in Chapter 3. For now, let’s create a data frame from scratch, which also provides an opportunity to introduce some useful basic functions.
Let’s make a vector of values and a vector of names (you might imagine they are treatment groups, for example). We can use the function rep() to repeat something as many times as we want. For example, let’s imagine we have two treatments each with 20 individuals, and that the average value of whatever we have measured is 5 for one group and 10 for the other group. In the code below we have nested several functions to achieve what we want to do. In the first line, we use the $\boldsymbol{c}$ () function to concatenate the words Group.A and Group.B into a single vector, then use the rep() function to repeat each value in that vector 20 times. What happens if you replace the argument “each=” with “times=?” In the second line, we concatenate together two vectors that are each 20 numbers long. In the third line we use the function data.frame() to create the new data frame from our two vectors and store it as an object called df .

As discussed previously, there is nothing special about the names chosen here. We could have called our vectors whatever we wanted, but the names treatment and values are sensible names to use for our purposes. Note that making the data frame dfl from the two vectors only works because we had already created the objects treatment and values. If you called your vectors “vecl” and “vec2,” you would need to modify the code where you create the data frame accordingly.

Earlier we use the function str() to look at the structure of a vector. The same function works for getting a quick look at a data frame. I think you will find that $\operatorname{str}()$ is one of the most useful functions there is. It quickly tells you what sort of data are found in each column in your data frame, as well as the size of the data frame. The number of “obs.” is the number of rows in the data frame and the number of “variables” is the number of columns.

## 应用统计代写

(SVL) 用于坦克 1，但将其大写用于坦克 2。达伦忘记在“蝌蚪”和“6”之间添加一个空格。R会将此类拼写错误视为单独和独立的，从而导致问题。显然，苏珊根本没有测量 2 号坦克的尾部，只是将单元格留空。达伦有两只没有可测量尾巴的蝌蚪，并在每个牢房中写下“无”字样。所有这些事情都会使数据无法分析。

1.9.1 向量

1. 向量(r1)

## 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。