### 统计代写|应用统计代写applied statistics代考|Introduction to R

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

## 统计代写|应用统计代写applied statistics代考|Introduction to R

The purpose of this first chapter is to introduce you to the basic workings of $R$ and get you up to speed. Some of this material might be familiar to you if you’ve used $R$ before, but the goal is to get anyone reading the book up to a basic level of familiarity. You will learn many of the basic and very important functions of $R$, such as:

• Creating objects
• Writing articulate R code
• Using functions
• Generating artificial data
• Entering data in a format that can be read and analyzed by $R$
This chapter does not intend to be an exhaustive introduction to all the basic workings of $R$. In other words, we’ll move pretty quickly here. If you would like a greater introduction, I highly recommend checking out the excellent book Getting Started With R: An Introduction for Biologists by Andrew Beckerman, Dylan Childs, and Owen Petchey.

$R$ is designed to be a small program (currently just about $80 \mathrm{mb}$ ) which makes it easy to download and install anywhere in the world. The base version of $R$ contains a great number of functions for organizing and analyzing data, but the real strength comes in what are called packages. Packages are freely downloadable additions to $R$ that provide new functions and datasets for particular analyses. For example, the base version of $R$ can conduct linear models and generalized linear models (Chapters $5-7$ ) but cannot conduct mixed effects models (Chapter 8). To do mixed effects models, you need to download a specific package (of which there are several).

The only important thing to remember about packages is that adding them to $\mathrm{R}$ is a two-step process. First, you have to install a package, which (perhaps counterintuitively) just downloads the package to your computer.Secondly, you have to load the package, which is when you have actively placed it in the current memory for use. You will generally obtain packages from the Comprehensive R Archive Network (https://cran.r-project.org/) (CRAN) directly through $R$.

## 统计代写|应用统计代写applied statistics代考|WORKING FROM THE SCRIPT WINDOW

The biggest mistake that most new $\mathrm{R}$ users make is to just type commands into the command prompt. The problem with this is that once you hit enter the command is gone. If you hit the up-arrow, $\mathrm{R}$ will scroll through the previously executed commands, but aside from this what you typed is gone and it cannot be edited! It is of course reasonable to run lines from the command line from time to time, but it is much better to work from a script window.

The script window allows you to easily save and edit your code, and to execute one or multiple lines of code at once. To open a blank script window, go to the File menu and click on New Document, or just hit command- $\mathrm{N}$ (Mac) or control- $\mathrm{N}$ (PC) on your keyboard.

In the script window you can type in your commands and then execute them by hitting command-enter (Mac) or control-R (PC). This means you type code into the script window and then the program sends the line of

code to the command prompt for you. Do not cut and paste code from the script window to the command prompt; that is a waste of time. You can also highlight multiple lines of code and execute them all at once. To save your code simply go to the File menu and save as you would any other file (or just hit command-S or control-S on your keyboard).

A script allows you to edit, run, and tweak your code, save it, return to it later, send it collaborators or mentors, and so on. Anything you think will want to run more than once, or that you might want to edit, should be typed into a script window (which is pretty much everything).

## 统计代写|应用统计代写applied statistics代考|Introduction to R

• 创建对象
• 编写清晰的 R 代码
• 使用函数
• 生成人工数据
• 以可读取和分析的格式输入数据R
本章并不打算详尽介绍所有的基本工作原理。R. 换句话说，我们将很快地移动到这里。如果您想要更详细的介绍，我强烈建议您阅读 Andrew Beckerman、Dylan Childs 和 Owen Petchey 撰写的优秀书籍 R 入门：生物学家介绍。

R被设计成一个小程序（目前大约80米b) 这使得在世界任何地方都可以轻松下载和安装。的基础版本R包含大量用于组织和分析数据的功能，但真正的优势在于所谓的包。软件包是可免费下载的附加组件R为特定分析提供新功能和数据集。例如，基础版本R可以进行线性模型和广义线性模型（章节5−7) 但不能进行混合效应模型（第 8 章）。要做混合效果模型，你需要下载一个特定的包（其中有几个）。

## 有限元方法代写

tatistics-lab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。