### 统计代写|生物统计学作业代写Biostatistics代考|DESCRIPTIVE METHODS FOR CATEGORICAL DATA

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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|生物统计学作业代写Biostatistics代考|PROPORTIONS

Many outcomes can be classified as belonging to one of two possible categories: presence and absence, nonwhite and white, male and female, improved and non-improved. Of course, one of these two categories is usually identified as of primary interest: for example, presence in the presence and absence classification, nonwhite in the white and nonwhite classification. We can, in general, relabel the two outcome categories as positive $(+)$ and negative $(-)$. An outcome is positive if the primary category is observed and is negative if the other category is observed.

It is obvious that in the summary to characterize observations made on a group of people, the number $x$ of positive outcomes is not sufficient; the group size $n$, or total number of observations, should also be recorded. The number $x$ tells us very little and becomes meaningful only after adjusting for the size $n$ of the group; in other words, the two figures $x$ and $n$ are often combined into a statistic, called a proportion:
$$p=\frac{x}{n}$$
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2 DESCRIPTIVE METHODS FOR CATEGORICAL DATA
The term statistic means a summarized figure from observed data. Clearly, $0 \leq p \leq 1$. This proportion $p$ is sometimes expressed as a percentage and is calculated as follows:
$$\text { percent }(\%)=\frac{x}{n}(100)$$

## 统计代写|生物统计学作业代写Biostatistics代考|Comparative Studies

Comparative studies are intended to show possible differences between two or more groups; Example $1.1$ is such a typical comparative study. The survey cited in Example $1.1$ also provided the following figures concerning boys in the group who use tobacco at least weekly. Among Asians, it was $9.7 \%$, followed by $11.6 \%$ of blacks, $20.6 \%$ of Hispanics, $25.4 \%$ of whites, and $38.3 \%$ of Native Americans.

In addition to surveys that are cross-sectional, as seen in Example 1.1, data for comparative studies may come from different sources; the two fundamental designs being retrospective and prospective. Retrospective studies gather past data from selected cases and controls to determine differences, if any, in exposure to a suspected risk factor. These are commonly referred to as case-control studies; each study being focused on a particular disease. In a typical casecontrol study, cases of a specific disease are ascertained as they arise from population-based registers or lists of hospital admissions, and controls are sampled either as disease-free persons from the population at risk or as hospitalized patients having a diagnosis other than the one under study. The advantages of a retrospective study are that it is economical and provides answers to research questions relatively quickly because the cases are already available. Major limitations are due to the inaccuracy of the exposure histories and uncertainty about the appropriateness of the control sample; these problems sometimes hinder retrospective studies and make them less preferred than pro-spective studies. The following is an example of a retrospective study in the field of occupational health.

## 统计代写|生物统计学作业代写Biostatistics代考|Screening Tests

Other uses of proportions can be found in the evaluation of screening tests or diagnostic procedures. Following these procedures, clinical observations, or laboratory techniques, people are classified as healthy or as falling into one of a number of disease categories. Such tests are important in medicine and epidemiologic studies and may form the basis of early interventions. Almost all such tests are imperfect, in the sense that healthy persons will occasionally be classified wrongly as being ill, while some people who are really ill may fail to be detected. That is, misclassification is unavoidable. Suppose that each person

in a large population can be classified as truly positive or negative for a particular disease; this true diagnosis may be based on more refined methods than are used in the test, or it may be based on evidence that emerges after the passage of time (e.g., at autopsy). For each class of people, diseased and healthy, the test is applied, with the results depicted in Figure 1.1.

The two proportions fundamental to evaluating diagnostic procedures are sensitivity and specificity. Sensitivity is the proportion of diseased people detected as positive by the test:
$$\text { sensitivity }=\frac{\text { number of diseased persons who screen positive }}{\text { total number of diseased persons }}$$
The corresponding errors are false negatives. Specificity is the proportion of healthy people detected as negative by the test:
$$\text { specificity }=\frac{\text { number of healthy persons who screen negative }}{\text { total number of healthy persons }}$$
and the corresponding errors are false positives.
Clearly, it is desirable that a test or screening procedure be highly sensitive and highly specific. However, the two types of errors go in opposite directions; for example, an effort to increase sensitivity may lead to more false positives, and vice versa.

p=Xn
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2 分类数据的描述方法

百分 (%)=Xn(100)

## 统计代写|生物统计学作业代写Biostatistics代考|Screening Tests

灵敏度 = 筛查呈阳性的患病人数  患病总人数

特异性 = 筛查阴性的健康人数量  健康人总数

## 广义线性模型代考

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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。