### 统计代写|生物统计代写biostatistics代考|INTRODUCTION TO BIOSTATISTICS

statistics-lab™ 为您的留学生涯保驾护航 在代写生物统计biostatistics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。我们的专家在代写生物统计biostatistics代写方面经验极为丰富，各种生物统计biostatistics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|生物统计代写biostatistics代考|WHAT IS BIOSTATISTICS

Biostatistics is the area of statistics that covers and provides the specialized methodology for collecting and analyzing biomedical and healthcare data. In general, the purpose of using biostatistics is to gather data that can be used to provide honest information about unanswered biomedical questions. In particular, biostatistics is used to differentiate between chance occurrences and possible causal associations, for identifying and estimating the effects of risk factors, for identifying the causes or predispositions related to diseases, for estimating the incidence and prevalence of diseases, for testing and evaluating the efficacy of new drugs or treatments, and for exploring and describing the well being of the general public.

A biostatistician is a scientist trained in statistics who also works in disciplines related to medical research and public health, who designs data collection procedures, analyzes data, interprets data analyses, and helps summarize the results of the studies. Biostatisticians may also develop and apply new statistical methodology required for analyzing biomedical data. Generally, a biostatistician works with a team of medical researchers and is responsible for designing the statistical protocol to be used in a study.

Biostatisticians commonly participate in research in the biomedical fields such as epidemiology, toxicology, nutrition, and genetics, and also often work for pharmaceutical companies. In fact, biostatisticians are widely employed in government agencies such as the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA). Biostatisticians are also employed by pharmaceutical companies, medical research units such as the MAYO Clinic and Fred Hutchison Cancer Research Center, Sloan-Kettering Institute, and many research universities. Furthermore, some biostatisticians serve on the editorial boards of medical journals and many serve as referees for biomedical journal articles in an effort to ensure the quality and integrity of data-based biomedical results that are published.

## 统计代写|生物统计代写biostatistics代考|POPULATIONS, SAMPLES, AND STATISTICS

In every biomedical study there will be research questions to define the particular population that is being studied. The population that is being studied is called the target population. The target population must be a well-defined population so that it is possible to collect representative data that can be used to provide information about the answers to the research questions. Finding the actual answer to a research question requires that the entire target population be observed, which is usually impractical or impossible. Thus, because it is generally impractical to observe the entire target population, biomedical researchers will use only a subset of the population units in their research study. A subset of the population is called a sample, and a sample may provide information about the answer to a research question but cannot definitively answer the question itself. That is, complete information on the target population is required to answer the research question, and because a sample is only a subset of the target population, it can only provide information about the answer. For this reason, statistics is often referred to as “the science of describing populations in the presence of uncertainty.”

The first thing a biostatistician generally must do is to take the research question and determine a particular set of characteristics of the target population that are related to the research question being studied. A biostatistician then must determine the relevant statistical questions about these population characteristics that will provide answers or the best information about the research questions. A characteristic of the target population that can be summarized numerically is called a parameter. For example, in a study of the body mass index (BMI) of teenagers, the average BMI value for the target population is a parameter, as is the percentage of teenagers having a BMI value less than 25 . The parameters of the target population are based on the information about the entire population, and hence, their values will be unknown to the researcher.

To have a meaningful statistical analysis, a researcher must have well-defined research questions, a well-defined target population, a well-designed sampling plan, and an observed sample that is representative of the target population. When the sample is representative of the target population, the resulting statistical analysis will provide useful information about the research questions; however, when the observed sample is not

representative of the target population the resulting statistical analysis will often lead to misleading or incorrect inferences being drawn about the target population, and hence, about the research questions, also. Thus, one of the goals of a biostatistician is to obtain a sample that is representative of the target population for estimating or testing the unknown parameters.

Once a representative sample is obtained, any quantity computed from the information in the sample and known values is called statistic. Thus, because any estimate of the unknown parameters will be based only on the information in the sample, the estimates are also statistics. Statements made by extrapolating from the sample information (i.e., statistics) about the parameters of the population are called statistical inferences, and good statistical inferences will be based on sound statistical and scientific reasoning. Thus, the statistical methods used by a biostatistician for making inferences need to be based on sound statistical and scientific reasoning. Furthermore, statistical inferences are meaningful only when they are based on data that are truly representative of the target population. Statistics that are computed from a sample are often used for estimating the unknown values of the parameters of interest, for testing claims about the unknown parameters, and for modeling the unknown parameters.

## 统计代写|生物统计代写biostatistics代考|Biomedical Studies

There are many different research protocols that are used in biomedical studies. Some protocols are forward looking studying what will happen in the future, some look at what has already occurred, and some are based on a cohort of subjects having similar characteristics. For example, the Framingham Heart Study is a large study conducted by the National Heart, Lung, and Blood Institute (NHLBI) that began in 1948 and continues today. The original goal of the Framingham Heart Study was to study the general causes of heart disease and stroke, and the three cohorts that have or are currently being studied in the Framingham Heart Study are as follows.

1. the original cohort that consists of a group of 5209 men and women between the ages of 30 and 62 recruited from Framingham, Massachusetts.
2. The second cohort, called the Offspring Cohort, consists of 5124 of the original participants’ adult children and their spouses.
3. the third cohort that consists of children of the Offspring Cohort. The third cohort is recruited with a planned target study size of 3500 grandchildren from members of the original cohort.

Two other large ongoing biomedical studies are the Women’s Health Initiative (WHI), which is a research study focusing on the health of women, and the National Health and Nutrition Examination Survey (NHANES), which is designed to assess the health and nutritional status of adults and children in the United States.

Several of the commonly used biomedical research protocols are described below.

• A cohort study is a research study carried out on a cohort of subjects. Cohort studies often involve studying the patients over a specified time period.
• A prospective study is a research study where the subjects are enrolled in the study and then followed forward over a period of time. In a prospective study, the outcome of interest has not yet occurred when the subjects are enrolled in the study.
• A retrospective study is a research study that looks backward in time. In a retrospective study, the outcome of interest has already occurred when the subjects are enrolled in the study.
• A case-control study is a research study in which subjects having a certain disease (cases) are compared with subjects who do not have the disease (controls).
• A longitudinal study is a research study where the same subjects are observed over an extended period of time.
• A cross-sectional study is a study to investigate the relationship between a response variable and the explanatory variables in a target population at a particular point in time.
• A blinded study is a research study where the subjects in the study are not told which treatment they are receiving. A research study is a double-blind study when neither the subject nor the staff administering the treatment know which treatment a subject is receiving.

## 统计代写|生物统计代写biostatistics代考|Biomedical Studies

1. 最初的队列由来自马萨诸塞州弗雷明汉的 5209 名年龄在 30 至 62 岁之间的男性和女性组成。
2. 第二个队列，称为后代队列，由 5124 名原始参与者的成年子女及其配偶组成。
3. 第三个队列由后代队列的孩子组成。第三个队列的计划目标研究规模为来自原始队列成员的 3500 名孙辈。

• 队列研究是对一组受试者进行的研究。队列研究通常涉及在特定时间段内研究患者。
• 前瞻性研究是一项研究，其中受试者被纳入研究，然后在一段时间内继续进行。在一项前瞻性研究中，当受试者参加研究时，感兴趣的结果尚未发生。
• 回顾性研究是一项回顾性研究。在一项回顾性研究中，感兴趣的结果在受试者参加研究时已经发生。
• 病例对照研究是一项研究，其中将患有某种疾病的受试者（病例）与没有患病的受试者（对照）进行比较。
• 纵向研究是一项研究，其中在较长时间内观察相同的受试者。
• 横断面研究是一项研究，旨在调查特定时间点目标人群中响应变量与解释变量之间的关系。
• 盲法研究是一项研究，其中研究中的受试者不被告知他们正在接受哪种治疗。当受试者和进行治疗的工作人员都不知道受试者正在接受哪种治疗时，研究是一项双盲研究。

## 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。