### 统计代写|贝叶斯分析代写Bayesian Analysis代考|Preliminary Considerations for Time Series

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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|贝叶斯分析代写Bayesian Analysis代考|Time Series

This chapter initiates the study of time series by presenting the necessary concepts that are essential for a good understanding of the subject. It begins with several examples of time series followed by techniques that will allow the investigator to understand the general characteristics of a time series. The first technique is to discern the general appearance of the series using plotting procedures, then from the plot form a subjective opinion of the trend of the series. More formal procedures using $R$ will reveal the various components (e.g., trend and seasonality) of the series. Based on the knowledge of the general appearance of the series, it allows one to propose a tentative model and see how well it fits the series. After a series of diagnostic checks, a final model is selected. Several examples of time series are now presented to illustrate the process of selecting a final model.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Airline Passenger Bookings

According to Cowpertwait and Metcalfe, the following $\mathrm{R}$ code will reveal the international airline booking, in thousands per month, for the airline Pan Am. This monthly information is also available from the Federal Aviation Administration for the years 1949-1960. See Table $3.1$
RC 3.1.
Data(Air Passengers)
$\mathrm{AP}<-$ Air Passengers
$A P$
The following $\mathrm{R}$ command summary provides the basic statistical characteristics (minimum, maximum, first and third quartiles, mean, and median) of the Air Passenger bookings.

summary (AP)

All information in $\mathrm{R}$ is stored in objects, and in this example, the object is class ts (time series), given by the following command:
class(AP)
[1] “ts”/.
It will be interesting to begin the analysis of this data set by determining its trend and by plotting the data.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Sunspot Data

The book by Feigelson and $B a b u^{2}$ is an excellent book on the use of statistics in astronomy. For the second example, one of the most interesting time series is the sunspot data.

The 1749 sunspot numbers are shown below, but for the total dataset, the link will provide the sunspot data and the corresponding plot of this interesting series.
http://solarscience.msfc.nasa.gov/SunspotCycle.shtml.
The numbers tabulated in SN_m_tot_V2.0.txt are the monthly averages of the daily sunspot number with error estimates as posted at the WDCSILSO, Royal Observatory of Belgium, Brussels.

$$\begin{array}{llllll} 1749 & 01 & 1749.042 & 96.7 & -1.0 & -1 \ 1749 & 02 & 1749.123 & 104.3 & -1.0 & -1 \ 1749 & 03 & 1749.204 & 116.7 & -1.0 & -1 \ 1749 & 04 & 1749.288 & 92.8 & -1.0 & -1 \ 1749 & 05 & 1749.371 & 141.7 & -1.0 & -1 \ 1749 & 06 & 1749.455 & 139.2 & -1.0 & -1 \ 1749 & 07 & 1749.538 & 158.0 & -1.0 & -1 \ 1749 & 08 & 1749.623 & 110.5 & -1.0 & -1 \ 1749 & 09 & 1749.707 & 126.5 & -1.0 & -1 \ \ldots . . & & \end{array}$$
Ancient Chinese astronomers as well as Galileo discovered that the sun has small dark spots that emerge on one side and disappear on the other side of the sun as it rotates. The period of this time series is approximately 30 days. According to the famous astronomer George Hale, the sunspots depend on the intensity of magnetic fields appearing on the surface of the sun. Now there is a branch of astronomy that is devoted to the solar physics that studies the morphology, evolution, and implications of these active solar areas. What is observable as a consequence of sunspot are solar flares which produce energetic particles and ionic phenomena that are not seen over most of the surface of the sun. Such flares can be disturbing in that they can have a major impact on the weather and on early communication systems. The number of spots observed on the sun and averaged over a month reveals a time series that has a large amplitude with obvious seasonality with a period of approximately 11 years. Refer to Feigelson and Babu , Pp. $422-423$ for a more detailed analysis of sunspot data. The fundamental characteristics (amplitudes, period, and harmonic components) of the sunspot cycle will be obvious from plotting the data.

class(AP)
[1] “ts”/。

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Sunspot Data

1749 个太阳黑子编号如下所示，但对于整个数据集，该链接将提供太阳黑子数据和这个有趣系列的对应图。
http://solarscience.msfc.nasa.gov/SunspotCycle.shtml。
SN_m_tot_V2.0.txt 中列出的数字是每日太阳黑子数量的月平均值，误差估计值发布在布鲁塞尔比利时皇家天文台 WDCSILSO。

1749011749.04296.7−1.0−1 1749021749.123104.3−1.0−1 1749031749.204116.7−1.0−1 1749041749.28892.8−1.0−1 1749051749.371141.7−1.0−1 1749061749.455139.2−1.0−1 1749071749.538158.0−1.0−1 1749081749.623110.5−1.0−1 1749091749.707126.5−1.0−1 …..

## 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。