## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|FRTN65

statistics-lab™ 为您的留学生涯保驾护航 在代写数据分析：从数据中学习Data Analytics: Learning from Data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。我们的专家在代写数据分析：从数据中学习Data Analytics: Learning from Data代写方面经验极为丰富，各种代写数据分析：从数据中学习Data Analytics: Learning from Data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|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Cleaning

We carefully define “data cleaning” and distinguish it from the data processing/pre-processing and data preparation. Data cleaning is the process to remove redundancies, correct errors, standardize the inconsistency of coding systems, unify the units of the variables, and combine the data or variables that were mistakenly spread out in different tables or wrong places. Some data cleaning examples include:

• Some patients may have multiple and conflicting race, gender or birthdate (age) records from different clinical visits (encounters) that need to be unified.
• The disease diagnosis table may use different disease coding systems such as ICD-9, ICD-10 or ICD-10-CM that need to be standardized.
• The same medication may have different spellings of its generic names.
• Some lab test results or clinical event measurements may use different units at different encounters for different patients.
• The procedure, surgical procedure and other tables may also use different coding systems that need to be standardized.

Data cleaning is not dependent on a particular project or a particular purpose of data analyses, it should be done for the whole FHR database since this is needed for all the projects and all the data analyses unless a project aims to study the errors of EHR data entry or data management. A challenge for data cleaning is that we may not be able to identify all the possible errors from the data and some errors are not trivial to detect. Thus, the cleaned data may still be error-prone, which may not be easy to handle.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Pre-Processing or Processing

Before we perform formal data analyses to address a clinical or scientific question, we must always process the data, summarize the data or extract useful features from the data in order to analyze the data or fit a statistical model to the data. Data pre-processing or processing may include the following tasks:

• Summarize the data into new data variables or generate some derived variables from the original data.
• Deal with the data issues: e.g., missing data imputation or redundant data combination.
• There are multiple timestamps for a clinical visit (encounter), medication, clinical event, procedure or other event and some of these timestamps are missing. A standardized timestamp is needed for each event and a rule for imputing the missing timestamp needs to be established for different purposes.
• Derive different features based on the repeated measurements of some variables.
• Extract features using feature engineering and dimension reduction approaches.

The data pre-processing or processing is project-specific. Different projects may require processing the data in various ways for different purposes. Usually, this step may need to be iteratively performed with the next step, data preparation, and these two steps may not have a clear boundary.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Cleaning

• 一些患者可能有来自不同临床访问（遭遇）的多重且相互冲突的种族、性别或出生日期（年龄）记录，需要统一。
• 疾病诊断表可能使用需要标准化的ICD-9、ICD-10或ICD-10-CM等不同的疾病编码系统。
• 同一种药物的通用名称可能有不同的拼写。
• 一些实验室测试结果或临床事件测量可能在不同患者的不同遭遇中使用不同的单位。
• 程序、手术程序和其他表格也可能使用需要标准化的不同编码系统。

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Pre-Processing or Processing

• 将数据汇总成新的数据变量或从原始数据中生成一些派生变量。
• 处理数据问题：例如，缺失数据插补或冗余数据组合。
• 临床访问（遭遇）、药物治疗、临床事件、程序或其他事件有多个时间戳，其中一些时间戳缺失。每个事件都需要一个标准化的时间戳，并且需要为不同的目的建立一个用于估算缺失时间戳的规则。
• 根据对某些变量的重复测量得出不同的特征。
• 使用特征工程和降维方法提取特征。

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## 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。

﻿

The graphs above are incomplete. These figures only show a vertex with degree four (vertex E), its nearest neighbors (A, B, C, and D), and segments of A-C Kempe chains. The entire graphs would also contain several other vertices (especially, more colored the same as B or D) and enough edges to be MPG’s. The left figure has A connected to $C$ in a single section of an A-C Kempe chain (meaning that the vertices of this chain are colored the same as A and C). The left figure shows that this A-C Kempe chain prevents B from connecting to $\mathrm{D}$ with a single section of a B-D Kempe chain. The middle figure has A and C in separate sections of A-C Kempe chains. In this case, B could connect to D with a single section of a B-D Kempe chain. However, since the A and C of the vertex with degree four lie on separate sections, the color of C’s chain can be reversed so that in the vertex with degree four, C is effectively recolored to match A’s color, as shown in the right figure. Similarly, D’s section could be reversed in the left figure so that D is effectively recolored to match B’s color.

Kempe also attempted to demonstrate that vertices with degree five are fourcolorable in his attempt to prove the four-color theorem [Ref. 2], but his argument for vertices with degree five was shown by Heawood in 1890 to be insufficient [Ref. 3]. Let’s explore what happens if we attempt to apply our reasoning for vertices with degree four to a vertex with degree five.

## 数学代写|图论作业代写Graph Theory代考|The previous diagrams

The previous diagrams show that when the two color reversals are performed one at a time in the crossed-chain graph, the first color reversal may break the other chain, allowing the second color reversal to affect the colors of one of F’s neighbors. When we performed the $2-4$ reversal to change B from 2 to 4 , this broke the 1-4 chain. When we then performed the 2-3 reversal to change E from 3, this caused C to change from 3 to 2 . As a result, F remains connected to four different colors; this wasn’t reversed to three as expected.
Unfortunately, you can’t perform both reversals “at the same time” for the following reason. Let’s attempt to perform both reversals “at the same time.” In this crossed-chain diagram, when we swap 2 and 4 on B’s side of the 1-3 chain, one of the 4’s in the 1-4 chain may change into a 2, and when we swap 2 and 3 on E’s side of the 1-4 chain, one of the 3’s in the 1-3 chain may change into a 2 . This is shown in the following figure: one 2 in each chain is shaded gray. Recall that these figures are incomplete; they focus on one vertex (F), its neighbors (A thru E), and Kempe chains. Other vertices and edges are not shown.

Note how one of the 3’s changed into 2 on the left. This can happen when we reverse $\mathrm{C}$ and $\mathrm{E}$ (which were originally 3 and 2 ) on E’s side of the 1-4 chain. Note also how one of the 4’s changed into 2 on the right. This can happen when we reverse B and D (which were originally 2 and 4) outside of the 1-3 chain. Now we see where a problem can occur when attempting to swap the colors of two chains at the same time. If these two 2’s happen to be connected by an edge like the dashed edge shown above, if we perform the double reversal at the same time, this causes two vertices of the same color to share an edge, which isn’t allowed. We’ll revisit Kempe’s strategy for coloring a vertex with degree five in Chapter $25 .$

## 数学代写|图论作业代写Graph Theory代考|The shading of one section of the B-R

• MPG 是三角测量的。它由具有三个边和三个顶点的面组成。
• 每个面的三个顶点必须是三种不同的颜色。
• 每条边由两个相邻的三角形共享，形成一个四边形。
• 每个四边形将有 3 或 4 种不同的颜色。如果与共享边相对的两个顶点恰好是相同的颜色，则它有 3 种颜色。
• 对于每个四边形，四个顶点中的至少 1 个顶点和最多 3 个顶点具有任何颜色对的颜色。例如，具有 R、G、B 和G有 1 个顶点R−是和3个顶点乙−G，或者您可以将其视为 1 个顶点乙−是和3个顶点G−R，或者您可以将其视为 BR 的 2 个顶点和 GY 的 2 个顶点。在后一种情况下，2G’ 不是同一链的连续颜色。
• 当您将更多三角形组合在一起（四边形仅组合两个）并考虑可能的颜色时，您将看到 Kempe 的部分

• 画一张R顶点和一个是由边连接的顶点。
• 如果一个新顶点连接到这些顶点中的每一个，它必须是乙或者G.
• 如果一个新顶点连接到 R 而不是是，可能是是,乙， 或者G.
• 如果一个新的顶点连接到是但不是R，可能是R,乙， 或者G.
• RY 链要么继续增长，要么被 B 包围，G.
• 如果你关注 B 和 G，你会为它的链条得出类似的结论。
• 如果一条链条完全被其对应物包围，则链条的新部分可能会出现在其对应物的另一侧。
Kempe 证明了所有具有四阶的顶点（那些恰好连接到其他四个顶点的顶点）都是四色的 [Ref. 2]。例如，考虑下面的中心顶点。

## 数学代写|图论作业代写Graph Theory代考|In the previous figure

• A 和 C 或者是 AC Kempe 链的同一部分的一部分，或者它们各自位于 AC Kempe 链的不同部分。（如果一种和C例如，是红色和黄色的，则 AC 链是红黄色链。） – 如果一种和C每个位于 AC Kempe 链的不同部分，其中一个部分的颜色可以反转，这有效地重新着色 C 以匹配 A 的颜色。如果 A 和 C 是 AC Kempe 链的同一部分的一部分，则 B 和 D每个都必须位于 BD Kempe 链的不同部分，因为 AC Kempe 链将阻止任何 BD Kempe 链从 B 到达 D。（如果乙和D是蓝色和绿色，例如，那么一种BD Kempe 链是蓝绿色链。）在这种情况下，由于 B 和 D 分别位于 BD Kempe 链的不同部分，因此 BD Kempe 链的其中一个部分的颜色可以反转，这有效地重新着色 D 以匹配 B颜色。– 因此，可以使 C 与 A 具有相同的颜色或使 D 具有与 A 相同的颜色乙通过反转 Kempe 链的分离部分。

Kempe 还试图证明五阶顶点是可四色的，以证明四色定理 [Ref. 2]，但 Heawood 在 1890 年证明他关于五次顶点的论点是不充分的 [Ref. 3]。让我们探讨一下如果我们尝试将我们对度数为四的顶点的推理应用于度数为五的顶点会发生什么。

## 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。

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|SEC595

statistics-lab™ 为您的留学生涯保驾护航 在代写数据分析：从数据中学习Data Analytics: Learning from Data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。我们的专家在代写数据分析：从数据中学习Data Analytics: Learning from Data代写方面经验极为丰富，各种代写数据分析：从数据中学习Data Analytics: Learning from Data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|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Initiate a Project

To initiate a project, one should start by proposing a research direction or topic, usually with a focus on a particular disease, treatment, medication, or other conditions of interest. Ideally, domain-specific clinicians, epidemiologists, or biomedical scientists in the multidisciplinary team may initiate a project with some potential biomedical or clinical hypotheses or scientific questions, although it may not need to be specific. Since the EHR database usually contains data from a large number of patients and covers many different discases, treatments and conditions, it is casy to raise many clinical, biomedical, or epidemiological questions. However, it may not be easy to identify a good question.
What is a good question? Based on our experience, a good research question based on the EHR database should satisfy the following criteria:

• Clinically or scientifically important and high-impact: If we could answer the question or prove/disprove the hypothesis, the results and conclusions are clinically important with a high impact so that we can publish the results in a high impact journal.
• Appropriate to use the available EHR data to address: The EHR data are appropriate or even the best data to address the proposed question or hypothesis. Sometimes the available EHR data may not be good or the best for the question or hypothesis. It is ideal if one can justify that using EHR data is the only way to address the proposed question or hypothesis and there is no other alternative.
• Appropriate and reliable endpoint or outcome data are available or can be derived from the $\mathrm{EHR}$ database for the proposed question or hypothesis. For any clinical or scientific question and hypothesis, appropriate endpoints or outcomes must be defined and identified, and sometimes good biomarkers can be used. It is necessary to confirm that these endpoint or outcome data are available and reliable in the EHR database. For example, to use mortality as the outcome or endpoint to evaluate a disease treatment, the researcher needs to carefully evaluate whether the EHR system captures the mortality reliably for most of the death cases due to the treatment. However, for chronic disease treatments, this may not be true since the follow-up time is usually not long enough to capture death due to the chronic diseases by the EHR system.
• The sample size is big enough: The sample size (the number of subjects, events and/or measurements) is usually quite large in the EHR database. However, for a particular question or hypothesis, we must screen the subjects based on the inclusion/exclusion criteria. For questions or hypotheses related to rare diseases or rare events, the sample size may still be an issue. Thus, it is also crucial to carefully define the study cohort based on the proposed question or hypothesis and develop the appropriate inclusion/exclusion criteria in order to ensure the sample size to be large enough.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Queries and Data Extraction

The EHR database should be converted into a structured relational database for easy querying and data extraction. Ideally, the database is converted into the OMOP Common Data Model (OHDSI 2020) that allows for the systematic analysis of disparate observational databases. The concept behind the OMOP Common Data Model (CDM) is to transform the data from different databases into a common format (data model) and a common representation (terminologies, vocabularies, coding schemes, etc.) so that systematic analyses can be performed using a library of standard analytic routines that have been written based on the common format.

Querying the database to extract the relevant data for a particular clinical research question is not trivial sometimes. To establish the inclusion/ exclusion criteria for data extraction, one needs to be familiar with the database structure and the terminologies, vocabularies and coding systems for diseases, medications, clinical events, vital signs, lab tests, procedures, etc. For example, to extract the data from all the patients with a particular disease diagnosis such as stroke, heart failure and diabetes, it may not be easy to get a full list of all disease diagnosis codes related to a particular disease. The keyword search of the disease code description may not get a complete list if the correct keywords related to the disease are not appropriately identified. The clinical domain experts need to be consulted for used to deal with this problem.
It is warranted to develop and use more robust data extraction approaches to avoid or minimize data extraction errors since the data analysis results and conclusions are not reliable if the data extraction error or uncertainty is too big. Sometimes, it may require redundant data extractions by multiple people and/or multiple approaches to avoid errors or mistakes. In order to use all the data from the EHR database to address a clinical question or hypothesis, one key principle for data extraction is to extract all the relevant data and not drop any data before exploring the data carefully. This suggests that all the data from all relevant subjects from all EHR tables should be extracted. Each of the variables in all the EHR tables should be considered and explored. The data at different encounters should be carefully reviewed. The time period for data extraction should be well defined for a particular clinical question or hypothesis.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Initiate a Project

• 具有临床或科学重要性和高影响力：如果我们能够回答问题或证明/反驳假设，则结果和结论具有高影响力的临床重要性，因此我们可以在高影响力期刊上发表结果。
• 适合使用可用的 EHR 数据来解决：EHR 数据适合，甚至是解决提出的问题或假设的最佳数据。有时，可用的 EHR 数据对于问题或假设可能不是很好或最好的。如果可以证明使用 EHR 数据是解决所提出的问题或假设的唯一方法并且没有其他替代方法，那将是理想的。
• 适当和可靠的终点或结果数据可用或可以从和HR提出的问题或假设的数据库。对于任何临床或科学问题和假设，必须定义和识别适当的终点或结果，有时可以使用良好的生物标志物。有必要确认这些终点或结果数据在 EHR 数据库中可用且可靠。例如，要使用死亡率作为评估疾病治疗的结果或终点，研究人员需要仔细评估 EHR 系统是否可靠地捕获了大多数因治疗而导致的死亡病例的死亡率。然而，对于慢性病治疗而言，这可能并非如此，因为随访时间通常不足以通过 EHR 系统捕获因慢性病导致的死亡。
• 样本量足够大：EHR 数据库中的样本量（受试者、事件和/或测量的数量）通常非常大。但是，对于特定的问题或假设，我们必须根据纳入/排除标准筛选受试者。对于与罕见疾病或罕见事件相关的问题或假设，样本量可能仍然是一个问题。因此，根据提出的问题或假设仔细定义研究队列并制定适当的纳入/排除标准以确保样本量足够大也至关重要。

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Data Queries and Data Extraction

EHR数据库应转换为结构化的关系数据库，以便于查询和数据提取。理想情况下，该数据库被转换为 OMOP 通用数据模型 (OHDSI 2020)，允许对不同的观测数据库进行系统分析。OMOP 通用数据模型 (CDM) 背后的概念是将来自不同数据库的数据转换为通用格式（数据模型）和通用表示（术语、词汇、编码方案等），以便可以使用执行系统分析基于通用格式编写的标准分析例程库。

﻿

﻿

﻿

## 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。

﻿

The graphs above are incomplete. These figures only show a vertex with degree four (vertex E), its nearest neighbors (A, B, C, and D), and segments of A-C Kempe chains. The entire graphs would also contain several other vertices (especially, more colored the same as B or D) and enough edges to be MPG’s. The left figure has A connected to $C$ in a single section of an A-C Kempe chain (meaning that the vertices of this chain are colored the same as A and C). The left figure shows that this A-C Kempe chain prevents B from connecting to $\mathrm{D}$ with a single section of a B-D Kempe chain. The middle figure has A and C in separate sections of A-C Kempe chains. In this case, B could connect to D with a single section of a B-D Kempe chain. However, since the A and C of the vertex with degree four lie on separate sections, the color of C’s chain can be reversed so that in the vertex with degree four, C is effectively recolored to match A’s color, as shown in the right figure. Similarly, D’s section could be reversed in the left figure so that D is effectively recolored to match B’s color.

Kempe also attempted to demonstrate that vertices with degree five are fourcolorable in his attempt to prove the four-color theorem [Ref. 2], but his argument for vertices with degree five was shown by Heawood in 1890 to be insufficient [Ref. 3]. Let’s explore what happens if we attempt to apply our reasoning for vertices with degree four to a vertex with degree five.

## 数学代写|图论作业代写Graph Theory代考|The previous diagrams

The previous diagrams show that when the two color reversals are performed one at a time in the crossed-chain graph, the first color reversal may break the other chain, allowing the second color reversal to affect the colors of one of F’s neighbors. When we performed the $2-4$ reversal to change B from 2 to 4 , this broke the 1-4 chain. When we then performed the 2-3 reversal to change E from 3, this caused C to change from 3 to 2 . As a result, F remains connected to four different colors; this wasn’t reversed to three as expected.
Unfortunately, you can’t perform both reversals “at the same time” for the following reason. Let’s attempt to perform both reversals “at the same time.” In this crossed-chain diagram, when we swap 2 and 4 on B’s side of the 1-3 chain, one of the 4’s in the 1-4 chain may change into a 2, and when we swap 2 and 3 on E’s side of the 1-4 chain, one of the 3’s in the 1-3 chain may change into a 2 . This is shown in the following figure: one 2 in each chain is shaded gray. Recall that these figures are incomplete; they focus on one vertex (F), its neighbors (A thru E), and Kempe chains. Other vertices and edges are not shown.

Note how one of the 3’s changed into 2 on the left. This can happen when we reverse $\mathrm{C}$ and $\mathrm{E}$ (which were originally 3 and 2 ) on E’s side of the 1-4 chain. Note also how one of the 4’s changed into 2 on the right. This can happen when we reverse B and D (which were originally 2 and 4) outside of the 1-3 chain. Now we see where a problem can occur when attempting to swap the colors of two chains at the same time. If these two 2’s happen to be connected by an edge like the dashed edge shown above, if we perform the double reversal at the same time, this causes two vertices of the same color to share an edge, which isn’t allowed. We’ll revisit Kempe’s strategy for coloring a vertex with degree five in Chapter $25 .$

## 数学代写|图论作业代写Graph Theory代考|The shading of one section of the B-R

• MPG 是三角测量的。它由具有三个边和三个顶点的面组成。
• 每个面的三个顶点必须是三种不同的颜色。
• 每条边由两个相邻的三角形共享，形成一个四边形。
• 每个四边形将有 3 或 4 种不同的颜色。如果与共享边相对的两个顶点恰好是相同的颜色，则它有 3 种颜色。
• 对于每个四边形，四个顶点中的至少 1 个顶点和最多 3 个顶点具有任何颜色对的颜色。例如，具有 R、G、B 和G有 1 个顶点R−是和3个顶点乙−G，或者您可以将其视为 1 个顶点乙−是和3个顶点G−R，或者您可以将其视为 BR 的 2 个顶点和 GY 的 2 个顶点。在后一种情况下，2G’ 不是同一链的连续颜色。
• 当您将更多三角形组合在一起（四边形仅组合两个）并考虑可能的颜色时，您将看到 Kempe 的部分

• 画一张R顶点和一个是由边连接的顶点。
• 如果一个新顶点连接到这些顶点中的每一个，它必须是乙或者G.
• 如果一个新顶点连接到 R 而不是是，可能是是,乙， 或者G.
• 如果一个新的顶点连接到是但不是R，可能是R,乙， 或者G.
• RY 链要么继续增长，要么被 B 包围，G.
• 如果你关注 B 和 G，你会为它的链条得出类似的结论。
• 如果一条链条完全被其对应物包围，则链条的新部分可能会出现在其对应物的另一侧。
Kempe 证明了所有具有四阶的顶点（那些恰好连接到其他四个顶点的顶点）都是四色的 [Ref. 2]。例如，考虑下面的中心顶点。

## 数学代写|图论作业代写Graph Theory代考|In the previous figure

• A 和 C 或者是 AC Kempe 链的同一部分的一部分，或者它们各自位于 AC Kempe 链的不同部分。（如果一种和C例如，是红色和黄色的，则 AC 链是红黄色链。） – 如果一种和C每个位于 AC Kempe 链的不同部分，其中一个部分的颜色可以反转，这有效地重新着色 C 以匹配 A 的颜色。如果 A 和 C 是 AC Kempe 链的同一部分的一部分，则 B 和 D每个都必须位于 BD Kempe 链的不同部分，因为 AC Kempe 链将阻止任何 BD Kempe 链从 B 到达 D。（如果乙和D是蓝色和绿色，例如，那么一种BD Kempe 链是蓝绿色链。）在这种情况下，由于 B 和 D 分别位于 BD Kempe 链的不同部分，因此 BD Kempe 链的其中一个部分的颜色可以反转，这有效地重新着色 D 以匹配 B颜色。– 因此，可以使 C 与 A 具有相同的颜色或使 D 具有与 A 相同的颜色乙通过反转 Kempe 链的分离部分。

Kempe 还试图证明五阶顶点是可四色的，以证明四色定理 [Ref. 2]，但 Heawood 在 1890 年证明他关于五次顶点的论点是不充分的 [Ref. 3]。让我们探讨一下如果我们尝试将我们对度数为四的顶点的推理应用于度数为五的顶点会发生什么。

## 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。

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|DATA2002

statistics-lab™ 为您的留学生涯保驾护航 在代写数据分析：从数据中学习Data Analytics: Learning from Data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。我们的专家在代写数据分析：从数据中学习Data Analytics: Learning from Data代写方面经验极为丰富，各种代写数据分析：从数据中学习Data Analytics: Learning from Data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|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Real-World Data and Real-World Evidence: Big Data in Practice

In this new era of advanced digital technologies, a huge amount of data is rapidly generated from all walks of life. In particular, the data generated from biomedical research fields and patient care practice are extremely valuable in improving public health and quality of life. Big Data is often a product of real-world practices and daily business operations and not specifically collected for research purposes. In order to use (or reuse) these real-world data to extract meaningful insights for scientific discoveries, there are many technical, analytical, and interpretation challenges and issues that need to be addressed. At the same time, great opportunities exist.

The real-world data such as the data from electronic medical record (EMR) or electronic health record (EHR) systems could provide real-world evidence for important clinical and scientific questions. These data can complement data from well-designed experiments and randomized controlled trials (RCTs), which are considered as the gold standard in clinical research and evidence-based medicine (Van Poucke et al. 2016, Frieden 2017). Usually, the environment and conditions for the designed experiments and RCTs are different from the real world and the inclusion/exclusion criteria of many RCTs are very restrictive since the designed experiments and RCTs are trying to control the noise levels and balance potential confounding factors between study groups. Usually, the confounding factors, including unknown or unmeasured confounding factors, are randomly balanced between treatment and control groups, thus, the conclusions from the well-designed experiments and RCTs are statistically and scientifically valid. However, the well-designed experiments and RCTs also have some limitations. First, the generalizability of the designed experiments and RCTs may be limited due to the following reasons: 1) the inclusion/exclusion criteria may be too restrictive; 2 ) the environment of designed experiments and RCTs may be different from the real-world situation; 3) subjects who consent to participate in a clinical trial may have significant differences in characteristics from those in a general population; and 4) the sample size is limited in a specific population for the convenience of subject recruitment, study implementation, and ethical considerations. In addition, the conclusion from RCTs is usually based on the population mean effect, and the individual effect of treatments or interventions are ignored. Moreover, the RCTs are often expensive with a high cost in labor and time and sometimes the RCTs are infeasible due to ethical issues and other reasons. That is why currently RCTs only provide support to $10-20 \%$ of clinical decisions (Mills, Thorlund, and Ioannidis 2013, Tricoci et al. 2009, McGinnis et al. 2013). It is necessary to use the data from observational studies and real-world data to provide complementary evidence for clinical decisions. Some literature suggests that the results from the RCTs and nonrandomized observational studies have strong agreements (Anglemyer, Horvath, and Bero 2014, Ioannidis et al. 2001). In addition, we also expect that real-world data could be used to better design and inform RCTs.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Use of EMR/EHR Database for Research

Electronic medical records (EMRs) are a digital version of the paper charts of patients in the hospital, clinic or clinician’s office. An EMR system usually contains the medical and treatment history of patients to help with the clinician’s decision on diagnosis and treatment for patient care. EMRs allow clinicians to better track patient’s data over time, identify and remind patients for preventive checkups and disease screenings, monitor patients and improve healthcare quality. Electronic health records (EHRs) are much broader than EMRs and contain all relevant health data of patients in addition to EMRs, which may include the data from laboratories, specialists, nursing homes and other healthcare providers. EHR systems are also designed to share the patient’s data with all authorized clinicians, caregivers, stakeholders, and even the patients themselves. Thus, a fully functional EHR system enables all authorized healthcare providers to access the latest information of patients anywhere and at any time so that more coordinated and patient-centered care can be provided timely to the patients. At the same time, the EHRs also serve as documentation for administration and billing purposes. Recently, EHR data became one of the major sources for real-world evidence to evaluate treatments, improve diagnosis and healthcare quality, reduce side effects and adverse events of drugs, predict disease risks and treatment outcomes, optimize and personalize patient care (MIT 2016).
Since FHR data are very complex and noisy, analysis and interpretation require sophisticated statistical methods and data science techniques as well as multidisciplinary collaborations between data scientists and domain experts. In addition, a novel data-driven research paradigm and state-ofthe-art approaches from a systematic perspective are necessary in order to harness a big EHR database and translate it into clinical knowledge for best practice. Based on our experience and from a systematic perspective, we summarize the procedure and the life cycle to use the EHR database for research and scientific discoveries in the following steps:

1. Initiate a project: proposing a research topic with some potential high-impact biomedical/clinical questions or hypotheses
2. Data queries and data extraction
3. Data cleaning
4. Data pre-processing or processing
5. Data preparation
6. Data analysis, modeling and prediction
7. Result validation
8. Result interpretation
9. Publication and dissemination
This procedure is quite similar to the data mining procedure for knowledge discoveries in databases (KDD) (McLachlan 2017, Fayyad, PiatetskyShapiro, and Smyth 1996, Fernández-Arteaga et al. 2016, Holzinger, Dehmer, and Jurisica 2014, Mitra, Pal, and Mitra 2002). We will provide the details and explanation for each of these steps in the following sections.

## 统计代写|数据分析：从数据中学习代写Data Analytics: Learning from Data代考|Use of EMR/EHR Database for Research

1. 启动一个项目：提出一个具有一些潜在的高影响生物医学/临床问题或假设的研究主题
2. 数据查询和数据提取
3. 数据清洗
4. 数据预处理或处理
5. 数据准备
6. 数据分析、建模和预测
7. 结果验证
8. 结果解读
9. 出版和传播
此过程与数据库中知识发现 (KDD) 的数据挖掘过程非常相似（McLachlan 2017, Fayyad, PiatetskyShapiro, and Smyth 1996, Fernández-Arteaga et al. 2016, Holzinger, Dehmer, and Jurisica 2014, Mitra ，帕尔和米特拉 2002）。我们将在以下各节中提供每个步骤的详细信息和解释。

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## 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。

﻿

The graphs above are incomplete. These figures only show a vertex with degree four (vertex E), its nearest neighbors (A, B, C, and D), and segments of A-C Kempe chains. The entire graphs would also contain several other vertices (especially, more colored the same as B or D) and enough edges to be MPG’s. The left figure has A connected to $C$ in a single section of an A-C Kempe chain (meaning that the vertices of this chain are colored the same as A and C). The left figure shows that this A-C Kempe chain prevents B from connecting to $\mathrm{D}$ with a single section of a B-D Kempe chain. The middle figure has A and C in separate sections of A-C Kempe chains. In this case, B could connect to D with a single section of a B-D Kempe chain. However, since the A and C of the vertex with degree four lie on separate sections, the color of C’s chain can be reversed so that in the vertex with degree four, C is effectively recolored to match A’s color, as shown in the right figure. Similarly, D’s section could be reversed in the left figure so that D is effectively recolored to match B’s color.

Kempe also attempted to demonstrate that vertices with degree five are fourcolorable in his attempt to prove the four-color theorem [Ref. 2], but his argument for vertices with degree five was shown by Heawood in 1890 to be insufficient [Ref. 3]. Let’s explore what happens if we attempt to apply our reasoning for vertices with degree four to a vertex with degree five.

## 数学代写|图论作业代写Graph Theory代考|The previous diagrams

The previous diagrams show that when the two color reversals are performed one at a time in the crossed-chain graph, the first color reversal may break the other chain, allowing the second color reversal to affect the colors of one of F’s neighbors. When we performed the $2-4$ reversal to change B from 2 to 4 , this broke the 1-4 chain. When we then performed the 2-3 reversal to change E from 3, this caused C to change from 3 to 2 . As a result, F remains connected to four different colors; this wasn’t reversed to three as expected.
Unfortunately, you can’t perform both reversals “at the same time” for the following reason. Let’s attempt to perform both reversals “at the same time.” In this crossed-chain diagram, when we swap 2 and 4 on B’s side of the 1-3 chain, one of the 4’s in the 1-4 chain may change into a 2, and when we swap 2 and 3 on E’s side of the 1-4 chain, one of the 3’s in the 1-3 chain may change into a 2 . This is shown in the following figure: one 2 in each chain is shaded gray. Recall that these figures are incomplete; they focus on one vertex (F), its neighbors (A thru E), and Kempe chains. Other vertices and edges are not shown.

Note how one of the 3’s changed into 2 on the left. This can happen when we reverse $\mathrm{C}$ and $\mathrm{E}$ (which were originally 3 and 2 ) on E’s side of the 1-4 chain. Note also how one of the 4’s changed into 2 on the right. This can happen when we reverse B and D (which were originally 2 and 4) outside of the 1-3 chain. Now we see where a problem can occur when attempting to swap the colors of two chains at the same time. If these two 2’s happen to be connected by an edge like the dashed edge shown above, if we perform the double reversal at the same time, this causes two vertices of the same color to share an edge, which isn’t allowed. We’ll revisit Kempe’s strategy for coloring a vertex with degree five in Chapter $25 .$

## 数学代写|图论作业代写Graph Theory代考|The shading of one section of the B-R

• MPG 是三角测量的。它由具有三个边和三个顶点的面组成。
• 每个面的三个顶点必须是三种不同的颜色。
• 每条边由两个相邻的三角形共享，形成一个四边形。
• 每个四边形将有 3 或 4 种不同的颜色。如果与共享边相对的两个顶点恰好是相同的颜色，则它有 3 种颜色。
• 对于每个四边形，四个顶点中的至少 1 个顶点和最多 3 个顶点具有任何颜色对的颜色。例如，具有 R、G、B 和G有 1 个顶点R−是和3个顶点乙−G，或者您可以将其视为 1 个顶点乙−是和3个顶点G−R，或者您可以将其视为 BR 的 2 个顶点和 GY 的 2 个顶点。在后一种情况下，2G’ 不是同一链的连续颜色。
• 当您将更多三角形组合在一起（四边形仅组合两个）并考虑可能的颜色时，您将看到 Kempe 的部分

• 画一张R顶点和一个是由边连接的顶点。
• 如果一个新顶点连接到这些顶点中的每一个，它必须是乙或者G.
• 如果一个新顶点连接到 R 而不是是，可能是是,乙， 或者G.
• 如果一个新的顶点连接到是但不是R，可能是R,乙， 或者G.
• RY 链要么继续增长，要么被 B 包围，G.
• 如果你关注 B 和 G，你会为它的链条得出类似的结论。
• 如果一条链条完全被其对应物包围，则链条的新部分可能会出现在其对应物的另一侧。
Kempe 证明了所有具有四阶的顶点（那些恰好连接到其他四个顶点的顶点）都是四色的 [Ref. 2]。例如，考虑下面的中心顶点。

## 数学代写|图论作业代写Graph Theory代考|In the previous figure

• A 和 C 或者是 AC Kempe 链的同一部分的一部分，或者它们各自位于 AC Kempe 链的不同部分。（如果一种和C例如，是红色和黄色的，则 AC 链是红黄色链。） – 如果一种和C每个位于 AC Kempe 链的不同部分，其中一个部分的颜色可以反转，这有效地重新着色 C 以匹配 A 的颜色。如果 A 和 C 是 AC Kempe 链的同一部分的一部分，则 B 和 D每个都必须位于 BD Kempe 链的不同部分，因为 AC Kempe 链将阻止任何 BD Kempe 链从 B 到达 D。（如果乙和D是蓝色和绿色，例如，那么一种BD Kempe 链是蓝绿色链。）在这种情况下，由于 B 和 D 分别位于 BD Kempe 链的不同部分，因此 BD Kempe 链的其中一个部分的颜色可以反转，这有效地重新着色 D 以匹配 B颜色。– 因此，可以使 C 与 A 具有相同的颜色或使 D 具有与 A 相同的颜色乙通过反转 Kempe 链的分离部分。

Kempe 还试图证明五阶顶点是可四色的，以证明四色定理 [Ref. 2]，但 Heawood 在 1890 年证明他关于五次顶点的论点是不充分的 [Ref. 3]。让我们探讨一下如果我们尝试将我们对度数为四的顶点的推理应用于度数为五的顶点会发生什么。

## 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。