## 统计代写|R语言代写R language代考|BIOF501

2022年12月27日

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

couryes-lab™ 为您的留学生涯保驾护航 在代写R语言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R语言代写方面经验极为丰富，各种代写R语言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
couryes™为您提供可以保分的包课服务

## 统计代写|R语言代写R language代考|Visualization methods

In an earlier image, we saw three very different distributions, all with the same mean and median. I said then that we need to quantify variance to tell them apart. In the following image, there are three very different distributions, all with the same mean, median, and variance.

If you just rely on basic summary statistics to understand univariate data, you’ll never get the full picture. It’s only when we visualize it that we can clearly see, at a glance, whether there are any clusters or areas with a high density of data points, the number of clusters there are, whether there are outliers, whether there is a pattern to the outliers, and so on. When dealing with univariate data, the shape is the most important part (that’s why this chapter is called Shape of Data!).

We will be using ggplot2’s qplot function to investigate these shapes and visualize these data. qplot (for quick plot) is the simpler cousin of the more expressive ggplot function. qplot makes it easy to produce handsome and compelling graphics using consistent grammar. Additionally, much of the skills, lessons, and know-how from qplot are transferrable to ggplot (for when we have to get more advanced).

where column is a particular column of the data frame dataframe, and the geom keyword argument specifies a geometric object – it will control the type of plot that we want. For visualizing univariate data, we don’t have many options for geom. The three types that we will be using are bar, histogram, and density. Making a bar graph of the frequency distribution of the number of carburetors couldn’t be easier: Using the factor function on the carb column makes the plot look better in this case.

## 统计代写|R语言代写R language代考|Multivariate data

In this chapter, we are going to describe relationships, and begin working with multivariate data, which is a fancy way of saying samples containing more than one variable.
The troublemaker reader might remark that all the datasets that we’ve worked with thus far (mtcars and airquality) have contained more than one variable. This is technically true-but only technically. The fact of the matter is that we’ve only been working with one of the dataset’s variables at any one time. Note that multivariate analytics is not the same as doing univariate analytics on more than one variable-multivariate analyses and describing relationships involve several variables at the same time.

To put this more concretely, in the last chapter we described the shape of, say, the temperature readings in the airquality dataset.

In this chapter, we will be exploring whether there is a relationship between temperature and the month in which the temperature was taken (spoiler alert: there is!).
The kind of multivariate analysis you perform is heavily influenced by the type of data that you are working with. There are three broad classes of bivariate (or two variable) relationships:

• The relationship between one categorical variable and one continuous variable
• The relationship between two categorical variables
• The relationship between two continuous variables
We will get into all of these in the next three sections. In the section after that, we will touch on describing the relationships between more than two variables. Finally, following in the tradition of the previous chapter, we will end with a section on how to create your own plots to capture the relationships that we’ll be exploring.

# R语言代写

## 统计代写|R语言代写R language代考|Multivariate data

• 一个分类变量和一个连续变量之间的关系
• 两个分类变量之间的关系
• 两个连续变量之间的关系
我们将在接下来的三个部分中讨论所有这些。在那之后的部分中，我们将涉及描述两个以上变量之间的关系。最后，按照上一章的传统，我们将以一节结束，介绍如何创建您自己的情节来捕捉我们将要探索的关系。

## 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。