### 统计代写|r语言作业代写代做|Skewness

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。

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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|r语言作业代写代考|Skewness

Skewness is the degree to which returns are asymmetric around their mean. Since a normal distribution is symmetric around the mean, skewness can be taken as one measure of how returns are not distributed normally. Why does skewness matter? If portfolio returns are right, or positively, skewed, it implies numerous small negative returns and a few large positive returns. If portfolio returns are left, or negatively, skewed, it implies numerous small positive returns and few large negative returns.
Here’s the equation for skewness:
$$\text { Skew }=\sum_{t=1}^{n}\left(x_{i}-\bar{x}\right)^{3} / n /\left(\sum_{t=1}^{n}\left(x_{i}-\bar{x}\right)^{2} / n\right)^{3 / 2}$$
Skewness has important substantive implications for risk and is also a concept that lends itself to data visualization. In fact, the visualizations are often more illuminating than the numbers themselves (though the numbers are what matter in the end). In this chapter, we will cover how to calculate skewness using xts and tidyverse methods, how to calculate rolling skewness and how to create several data visualizations as pedagogical aids.

## 统计代写|r语言作业代写代考|Rolling Skewness in the xts world

For the same reasons that we did so with standard deviation, let’s check whether we have missed anything unusual in the portfolio’s historical tail risk by examining rolling skewness.

In the xts world, calculating rolling skewness is almost identical to calculating rolling standard deviation, except we call the skewness () function instead of StdDev(). Since this is a rolling calculation, we need a period of time and will use a 24 -month window.

As we saw with standard deviation, passing a rolling calculation to dplyr pipes does not work smoothly. We can, though, use rollify() from tibbletime.
We first create a rolling function. We then convert our portfolio returns to a tibbletime object and pass them to the rolling function.

## 统计代写|r语言作业代写代考|Visualizing Rolling Skewness

Our visualization flow for skewness is quite similar to our work on standard deviation. We start by passing rolling_skew_xts into highcharter. We also tweak our $y$-axis to capture the nature of the rolling fluctuations by setting the range to between 2 and $-2$ with hc_yAxis $(\ldots, \max =2, \min =-2)$.

Figure $5.7$ shows the movement in rolling skewness, try re-running the code without enforcing limits on the $y$-axis.

We create a similar visualization with ggplot() and our rolling_skew_tq object.

I will again impose minimum and maximum $y$-axis values, with scale_y_continuous(limits $=c(-1,1) \ldots$ ).

FIGURE 5.8: Rolling Skewness ggplot
Figure $5.8$ makes the rolling skewness seem more volatile than Figure 5.7. Tweaking the $y$-axis can have a big effect, use it wisely.

The rolling charts are quite illuminating and show that the 24-month skewness has been positive for about half the lifetime of this portfolio even though the overall skewness is negative. Normally we would now head to Shiny and enable a way to test different rolling windows but let’s wait until we cover kurtosis in the next chapter.

## 统计代写|r语言作业代写代考|Skewness

偏斜 =∑吨=1n(X一世−X¯)3/n/(∑吨=1n(X一世−X¯)2/n)3/2

## 广义线性模型代考

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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。