### 统计代写|贝叶斯统计代写Bayesian statistics代考|Areal unit data sets used in the book

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯统计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。我们的专家在代写贝叶斯统计代写方面经验极为丰富，各种代写贝叶斯统计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|贝叶斯统计代写beyesian statistics代考|Covid-19 mortality data from England

This data set presents the number of deaths due to Covid- 19 during the peak from March 13 to July 31,2020 in the 313 Local Authority Districts, Counties and Unitary Authorities (LADCUA) in England; see Figure 1.8. There are 49,292 weekly recorded deaths during this period of 20 weeks. Figure $1.9$ shows a map of the number of deaths and the death rate per 100,000 people in each of the 313 LADCUAs. Contrasting the two plots, it is clear that much spatial variation is seen in the right panel of the death rates per 100,000 people. The boxplot of the weekly death rates shown in Figure $1.10$ shows the first peak during weeks 15 and 16 (April 10th to 23 rd) and a very slow decline of the death numbers after the peak. The main purpose here is to model the spatiotemporal variation in the death rates. This data set will be used as a running example for all the areal unit data models in Chapter 10. Chapter 3 provides some further preliminary exploratory analysis of this data set.

The Demographic and Health Surveys (DHS) program ${ }^{1}$ routinely collects several data sets for monitoring health at a global level. This example is based on a 2014 vaccination coverage data set for the country Kenya in East Africa. The data set contains the number of children aged 12-23 months who had received the first dose of measles-containing vaccine (MCV1) at any time before the survey in 2014. Figure $1.11$ plots the observed vaccination proportions in 2014. A substantial analysis of this and several related data sets has been conducted by Utazi et al. (2021). Modeled in Section 11.2, this example aims to assess vaccination coverage rates in the different counties in Kenya.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Cancer rates in the United States

The Centers for Disease Control and Prevention in the United States provides downloadable cancer rate data at various geographical levels, e.g. the 50 states. Such a data set can be downloaded along with various information e.g. gender and ethnicity and types of cancer. However, due to the data identifiability and data protection reasons, some of the smaller rate counts (which arises due to finer classification by the factors) rates are not made public. Hence, for the purposes of illustration of this book, we aim to model aggregated annual data at the state level. The full data set provides state-wise annual rates of cancer from all causes during from 2003 to 2017 . Figure $1.12$ provides a map of the aggregated cancer rates per 100,000 people from all causes during from 2003 to 2017 for the 48 contiguous states. This is an example of a choropleth map that uses shades of color or gray scale to classify values into a few broad classes, like a histogram. The figure shows higher total incidence rates in the northeast compared to south-west. Florida also shows a higher rate which may be attributed to a larger share of the retired elderly residents in the state. The full spatio-temporal data set will be analyzed in Section 11.3.

The observed standardized mortality rates, see discussion in Section $11.3$ on how to obtain those, for ten selected states are shown in Figure 1.13. These states are hand-picked to represent the full range of the SMR values. The research question that is of interest here is, “is there an upward trend in these rates after accounting for spatio-temporal correlation and any other important fixed effects covariates?” This is investigated in Section 11.3.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Hospitalization data from England

Monthly counts of the numbers of hospitalizations due to respiratory diseases from the 323 Local and Unitary Authorities (LUA) in England for the 60 months in the 5 -year period 2007 to 2011 are available from the study published by Lee et al. (2017). These counts depend on the size and demographics of the population at risk, which are adjusted for by computing the expected number of hospital admissions $E_{i t}$ using what is known as indirect standardization, see Section $2.12$, from national age and sex-specific hospitalization rates in England.

In this example, the study region is England, UK, partitioned into $i=$ $1, \ldots, n=323$ Local and Unitary Authorities (LUA), and data are available for $t=1, \ldots, T=60$ months between 2007 and 2011 . Counts of the numbers of respiratory hospitalizations for LUA $i$ and month $t$ are denoted by $Y_{i t}$, for $i=1, \ldots, 323$ and $t=1, \ldots, 60$, which have a median value of 111 and a range from 6 to 2485 . The monthly time scale matches the study by Greven et al. (2011), whereas the majority of studies such as Lee et al. (2009) utilize yearly data. An advantage of the monthly scale is that it requires less aggregation of the data away from the individual level, but it does mean that $Y_{\text {it }}$ could include admissions driven by both chronic and acute pollution exposure.
The spatial (left panel) and temporal (bottom panel) patterns in the Standardized Morbidity Ratio, $\mathrm{SMR}{i t}=Y{i t} / E_{\text {it }}$ are displayed in Figure $1.14$, where a value of $1.2$ corresponds to a $20 \%$ increased risk compared to $E_{i t}$. The figure shows the highest risks are in cities in the center and north of England, such as Birmingham, Leeds and Manchester, while the temporal pattern is strongly seasonal, with higher risks of admission in the winter due to factors such as influenza epidemics and cold temperature. This data set is used as an example in Section $11.4$ of this book.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Hospitalization data from England

2007 年至 2011 年 5 年期间 60 个月内英格兰 323 个地方和单一当局 (LUA) 因呼吸系统疾病住院人数的月度统计可从 Lee 等人发表的研究中获得。（2017）。这些计数取决于高危人群的规模和人口统计数据，通过计算预期住院人数进行调整和一世吨使用所谓的间接标准化，请参阅第2.12，来自英格兰的国家年龄和性别特定住院率。

## 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。