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

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生物统计学是将统计技术应用于健康相关领域的科学研究,包括医学、生物学和公共卫生,并开发新的工具来研究这些领域。

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我们提供的生物统计biostatistics及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (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|INTRODUCTION TO BIOSTATISTICS

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|INTRODUCTION TO BIOSTATISTICS

生物统计代考

统计代写|生物统计代写biostatistics代考|WHAT IS BIOSTATISTICS

生物统计学是涵盖并提供用于收集和分析生物医学和医疗保健数据的专门方法的统计领域。一般来说,使用生物统计学的目的是收集可用于提供有关未回答的生物医学问题的诚实信息的数据。特别是,生物统计学用于区分偶然事件和可能的因果关系,识别和估计风险因素的影响,确定与疾病相关的原因或易感性,估计疾病的发病率和流行率,测试和评估风险因素的影响。新药或新疗法的疗效,以及探索和描述公众的福祉。

生物统计学家是受过统计学培训的科学家,也从事与医学研究和公共卫生相关的学科工作,负责设计数据收集程序、分析数据、解释数据分析并帮助总结研究结果。生物统计学家还可以开发和应用分析生物医学数据所需的新统计方法。通常,生物统计学家与一组医学研究人员合作,并负责设计用于研究的统计方案。

生物统计学家通常参与流行病学、毒理学、营养学和遗传学等生物医学领域的研究,也经常为制药公司工作。事实上,生物统计学家广泛受雇于政府机构,如美国国立卫生研究院 (NIH)、疾病控制和预防中心 (CDC)、食品和药物管理局 (FDA) 和环境保护署 (EPA)。生物统计学家还受雇于制药公司、医学研究单位(如 MAYO 诊所和 Fred Hutchison 癌症研究中心)、Sloan-Kettering 研究所以及许多研究型大学。此外,

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

在每项生物医学研究中,都会有研究问题来定义正在研究的特定人群。正在研究的人群称为目标人群。目标人群必须是明确定义的人群,以便可以收集代表性数据,这些数据可用于提供有关研究问题答案的信息。找到研究问题的实际答案需要观察整个目标人群,这通常是不切实际或不可能的。因此,由于观察整个目标人群通常是不切实际的,生物医学研究人员将在他们的研究中仅使用人口单位的一个子集。总体的一个子集称为样本,样本可能会提供有关研究问题答案的信息,但不能明确回答问题本身。也就是说,回答研究问题需要目标人群的完整信息,而由于样本只是目标人群的一个子集,它只能提供关于答案的信息。出于这个原因,统计数据通常被称为“在存在不确定性的情况下描述人口的科学”。

生物统计学家通常必须做的第一件事是接受研究问题并确定与正在研究的研究问题相关的目标人群的一组特定特征。然后,生物统计学家必须确定有关这些人口特征的相关统计问题,这些问题将提供有关研究问题的答案或最佳信息。可以用数字概括的目标人群的特征称为参数。例如,在一项关于青少年体重指数(BMI)的研究中,目标人群的平均 BMI 值是一个参数,BMI 值低于 25 的青少年百分比也是一个参数。目标人群的参数基于整个人群的信息,因此,

为了进行有意义的统计分析,研究人员必须有明确的研究问题、明确的目标人群、精心设计的抽样计划以及代表目标人群的观察样本。当样本代表目标人群时,由此产生的统计分析将提供有关研究问题的有用信息;然而,当观察到的样本不是

代表目标人群的结果统计分析通常会导致对目标人群的误导或不正确的推论,因此也对研究问题产生误导。因此,生物统计学家的目标之一是获得代表目标人群的样本,用于估计或测试未知参数。

一旦获得有代表性的样本,根据样本中的信息和已知值计算出的任何数量都称为统计量。因此,由于对未知参数的任何估计都将仅基于样本中的信息,因此估计也是统计数据。从样本信息(即统计数据)中推断出关于总体参数的陈述称为统计推断,良好的统计推断将基于合理的统计和科学推理。因此,生物统计学家用于推断的统计方法需要基于合理的统计和科学推理。此外,统计推断只有在基于真正代表目标人群的数据时才有意义。

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

生物医学研究中使用了许多不同的研究方案。一些协议具有前瞻性,研究未来会发生什么,一些着眼于已经发生的事情,还有一些基于具有相似特征的一组受试者。例如,弗雷明汉心脏研究是由国家心肺血液研究所 (NHLBI) 进行的一项大型研究,该研究始于 1948 年,一直持续到今天。弗雷明汉心脏研究的最初目标是研究心脏病和中风的一般原因,弗雷明汉心脏研究已经或目前正在研究的三个队列如下。

  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。

另外两项正在进行的大型生物医学研究是妇女健康倡议 (WHI),这是一项关注女性健康的研究,以及全国健康和营养检查调查 (NHANES),旨在评估妇女的健康和营养状况。美国的成人和儿童。

下面描述了几种常用的生物医学研究方案。

  • 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。
统计代写|生物统计代写biostatistics代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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