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AP 统计主要是介绍收集、分析和从数据中得出结论的主要概念和工具。
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我们提供的AP统计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 高等楖率论
- 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|AP统计作业代写代考|Planning a Statistical Study
Planning a statistical study and gathering data are essential components of obtaining réliable information. Depending on the naturé of the statistical study, à great deal of expertise and resources may be required during the planning stage. In this section, we look at some of the basics of planning a statistical study.One issue to consider is whether to use the entire population in a study or a representative sample. If we use data from the entire population, we have a census.
When the population is small and easily accessible, a census is very useful because it gives complete information about the population. However, obtaining a census can be both expensive and difficult. Every 10 years, the U.S. Department of Commerce Census Bureau is required to conduct a census of the United States. However, contacting some members of the population-such as the homeless-is almost impossible. Sometimes members of the population will not respond. In such cases, statistical estimates for the missing responses are often supplied.
Overcounting, that is, counting the same person more than once, is also a problem the Census Bureau is addressing. In fact, in 2000 , slightly more people were counted twice than the estimated number of people missed. For instance, a college student living on campus might be counted on a parent’s census form as well as on his or her own census form.If we use data from only part of the population of interest, we have a sample.
In the previous section, we examined several sampling strategies: simple random, stratified, cluster, systematic, multistage, and convenience. In this text, we will study methods of inferential statistics based on simple random samples.
As discussed in Section 1.2, simulation is a numerical facsimile of real-world phenomena. Sometimes simulation is called a “dry lab” approach, in the sense that it is a mathematical imitation of a real situation. Advantages of simulation are that numerical and statistical simulations can fit real-world problems extremely well. The researcher can also explore procedures through simulation that might be very dan gerous in real life.
统计代写|AP统计作业代写代考|Experiment
In 1778 , Captain James Cook landed in what we now call the Hawaiian Islands. He gave the islanders a present of several goats, and over the years these animals multiplied into wild herds totaling several thousand. They eat almost anything, including the famous silver sword plant, which was once unique to Hawaii. At one time, the silver sword grew abundantly on the island of Maui (in Haleakala, a national park on that island, the silver sword can still be found), but each year there seemed to be fewer and fewer plants. Biologists suspected that the goats were partially responsible for the decline in the number of plants and conducted a statistical study that verified their theory.
(a) To test the theory, park biologists set up stations in remote areas of Haleakala. At each station two plots of land similar in soil conditions, climate, and plant count were selected. Onc plot was fenced to kecp out the goats, while the other was not. At regular intervals a plant count was made in each plot. This study involved an experiment because a treatment (the fence) was imposed on one plot.
(b) The experiment involved two plots at each station. The plot that was not fenced represented the control plot. This was the plot on which a treatment was specifically not imposed, although the plot was similar to the fenced plot in every other way.Statistical experiments are commonly used to determine the effect of a treatment. However, the design of the experiment needs to control for other possible causes of the effect. For instance, in medical experiments, the placebo effect is the improvement or change that is the result of patients just believing in the treatment, whether or not the treatment itself is effective.
To account for the placebo effect, patients are divided into two groups. One group receives the prescribed treatment. The other group, called the control group, receives a dummy or placebo treatment that is disguised to look like the real treatment. Finally, after the treatment cycle, the medical condition of the patients in the treatment group is compared to that of the patients in the control group.
A common way to assign patients to treatment and control groups is by using a random process. This is the essence of a completely randomized experiment.
统计代写|AP统计作业代写代考|Completely Randomized Experiment
Can chest pain be relieved by drilling holes in the heart? For more than a decade, surgeons have been using a laser procedure to drill holes in the heart. Many patients report a lasting and dramatic decrease in angina (chest pain) symptoms. Is the relief due to the procedure, or is it a placebo effect? A recent research project at Lenox Hill Hospital in New York City provided some information about this issue by using a completely randomized experiment. The laser treatment was applied through a less invasive (catheter laser) process. A group of 298 volunteers with severe, untreatable chest pain were randomly assigned to get the laser or not. The patients were sedated but awake. They could hear the doctors discuss the laser process. Each patient thought he or she was receiving the treatment.
The experimental design can be pictured as
Patients
with chest $\rightarrow$ Random
pain $\rightarrow$ Group 1
149 patients $\rightarrow$ Lreatment 1
Laser holes in heart $\rightarrow$ Ciroup 2
149 patients $\rightarrow$ No holes in heart
The laser patients did well. But shockingly, the placebo group showed more improvement in pain relief. The medical impacts of this study are still being investigated.
It is difficult to control all the variables that might influence the response to a treatment. One way to control some of the variables is through blocking.
A randomized block design utilizing gender for blocks in the experiment involving laser holes in the heart would be
The study cited in Example 5 has many features of good experimental design.
AP统计代写
统计代写|AP统计作业代写代考|Planning a Statistical Study
计划统计研究和收集数据是获得可靠信息的重要组成部分。根据统计研究的性质，在规划阶段可能需要大量的专业知识和资源。在本节中，我们将了解规划统计研究的一些基础知识。要考虑的一个问题是在研究中使用整个人群还是使用代表性样本。如果我们使用来自整个人口的数据，我们就有了人口普查。
当人口较少且易于访问时，人口普查非常有用，因为它提供了有关人口的完整信息。然而，进行人口普查既昂贵又困难。每隔 10 年，美国商务部人口普查局必须对美国进行一次人口普查。然而，联系一些人口中的成员——比如无家可归者——几乎是不可能的。有时，人口中的成员不会回应。在这种情况下，通常会提供缺失响应的统计估计值。
多统计，即多次统计同一个人，也是人口普查局正在解决的问题。事实上，在 2000 年，被统计的人数比估计的错过人数略多两倍。例如，住在校园里的大学生可能会被计算在父母的人口普查表以及他或她自己的人口普查表上。如果我们只使用来自感兴趣人口的一部分的数据，我们就有了样本。
在上一节中，我们研究了几种抽样策略：简单随机、分层、集群、系统、多阶段和便利。在本文中，我们将研究基于简单随机样本的推理统计方法。
如第 1.2 节所述，模拟是对现实世界现象的数字复制。有时模拟被称为“干实验室”方法，因为它是对真实情况的数学模拟。模拟的优点是数值和统计模拟可以非常好地适应现实世界的问题。研究人员还可以通过模拟探索在现实生活中可能非常危险的程序。
统计代写|AP统计作业代写代考|Experiment
1778 年，詹姆斯库克船长登陆了我们现在所说的夏威夷群岛。他送给岛民几只山羊作为礼物，这些年来这些动物繁殖成数以千计的野群。他们几乎什么都吃，包括曾经是夏威夷独有的著名银剑植物。曾几何时，银剑在茂宜岛（在该岛的国家公园哈雷阿卡拉，至今仍能找到银剑）上生长茂盛，但每年的植物似乎越来越少。生物学家怀疑山羊是植物数量下降的部分原因，并进行了一项统计研究来验证他们的理论。
(a) 为了验证这一理论，公园生物学家在哈雷阿卡拉的偏远地区设立了观测站。在每个站点选择两块土壤条件、气候和植物数量相似的土地。Onc地块被围起来以防止山羊进入，而另一个则没有。定期对每个小区进行植物计数。这项研究涉及一项实验，因为对一个地块施加了处理（围栏）。
(b) 实验涉及每个站点的两个地块。没有围栏的地块代表控制地块。这是没有特别施加处理的地块，尽管该地块在其他方面与围栏地块相似。统计实验通常用于确定处理的效果。然而，实验的设计需要控制影响的其他可能原因。例如，在医学实验中，安慰剂效应是患者仅仅相信治疗而产生的改善或改变，无论治疗本身是否有效。
为了解释安慰剂效应，将患者分为两组。一组接受规定的治疗。另一组，称为对照组，接受假治疗或安慰剂治疗，伪装成看起来像真正的治疗。最后，在治疗周期后，将治疗组患者的医疗状况与对照组患者的医疗状况进行比较。
将患者分配到治疗组和对照组的常用方法是使用随机过程。这是完全随机实验的本质。
统计代写|AP统计作业代写代考|Completely Randomized Experiment
在心脏上钻孔可以缓解胸痛吗？十多年来，外科医生一直在使用激光手术在心脏上钻孔。许多患者报告心绞痛（胸痛）症状持续显着减少。缓解是由于手术，还是安慰剂效应？纽约市莱诺克斯山医院最近的一项研究项目通过使用完全随机的实验提供了有关此问题的一些信息。激光治疗是通过侵入性较小的（导管激光）过程进行的。一组 298 名患有严重、无法治愈的胸痛的志愿者被随机分配接受或不接受激光治疗。患者被镇静但清醒。他们可以听到医生讨论激光过程。每个病人都认为他或她正在接受治疗。
实验设计可以被描绘为胸部
患者
→随机
疼痛→第 1
149 组患者→Lreatment 1
心脏激光孔→Ciroup 2
149 名患者→心脏没有洞
激光患者表现良好。但令人震惊的是，安慰剂组在缓解疼痛方面表现出更大的改善。这项研究的医学影响仍在调查中。
很难控制所有可能影响治疗反应的变量。控制某些变量的一种方法是通过阻塞。
在涉及心脏激光孔的实验中，利用性别进行块的随机块设计将是
示例 5 中引用的研究具有良好实验设计的许多特征。
Course Overview
AP Statistics is an introductory college-level statistics course that introduces students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Students cultivate their understanding of statistics using technology, investigations, problem solving, and writing as they explore concepts like variation and distribution; patterns and uncertainty; and data-based predictions, decisions, and conclusions.
- AP Statistics Course OverviewThis resource provides a succinct description of the course and exam.PDF180.39 KB
- AP Statistics Course and Exam Description Walk-ThroughLearn more about the CED in this interactive walk-through.
- AP Statistics Course at a GlanceExcerpted from the AP Statistics Course and Exam Description, the Course at a Glance document outlines the topics and skills covered in the AP Statistics course, along with suggestions for sequencing.PDF585.66 KB
- AP Statistics Course and Exam DescriptionThis is the core document for this course. Unit guides clearly lay out the course content and skills and recommend sequencing and pacing for them throughout the year. The CED was updated in March 2021.PDF17.9 MB
- AP Statistics CED Errata SheetThis document details the updates made to the course and exam description (CED) in March 2021.PDF934.27 KB
- AP Statistics CED Scoring GuidelinesThis document details how each of the sample free-response questions in the course and exam description (CED) would be scored. This information is now in the online CED, but was not included in the binders teachers received in 2019.PDF245.87 KB
Course Content
Based on the Understanding by Design® (Wiggins and McTighe) model, this course framework provides a clear and detailed description of the course requirements necessary for student success. The framework specifies what students must know, be able to do, and understand, with a focus on three big ideas that encompass the principles and processes in the discipline of statistics. The framework also encourages instruction that prepares students for advanced coursework in statistics or other fields using statistical reasoning and for active, informed engagement with a world of data to be interpreted appropriately and applied wisely to make informed decisions.
The AP Statistics framework is organized into nine commonly taught units of study that provide one possible sequence for the course. As always, you have the flexibility to organize the course content as you like.
| Unit | Exam Weighting (Multiple-Choice Section) |
| Unit 1: Exploring One-Variable Data | 15%–23% |
| Unit 2: Exploring Two-Variable Data | 5%–7% |
| Unit 3: Collecting Data | 12%–15% |
| Unit 4: Probability, Random Variables, and Probability Distributions | 10%–20% |
| Unit 5: Sampling Distributions | 7%–12% |
| Unit 6: Inference for Categorical Data: Proportions | 12%–15% |
| Unit 7: Inference for Quantitative Data: Means | 10%–18% |
| Unit 8: Inference for Categorical Data: Chi-Square | 2%–5% |
| Unit 9: Inference for Quantitative Data: Slopes | 2%–5% |
Course Skills
The AP Statistics framework included in the course and exam description outlines distinct skills that students should practice throughout the year—skills that will help them learn to think and act like statisticians.
| Skill | Description | Exam Weighting (Multiple-Choice Section) |
| 1. Selecting Statistical Methods | Select methods for collecting and/or analyzing data for statistical inference. | 15%–23% |
| 2. Data Analysis | Describe patterns, trends, associations, and relationships in data. | 15%–23% |
| 3. Using Probability and Simulation | Explore random phenomena. | 30%–40% |
| 4. Statistical Argumentation | Develop an explanation or justify a conclusion using evidence from data, definitions, or statistical inference. | 25%–35% |
统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考
随机过程代考
在概率论概念中，随机过程是随机变量的集合。 若一随机系统的样本点是随机函数，则称此函数为样本函数，这一随机系统全部样本函数的集合是一个随机过程。 实际应用中，样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化，随机运动如布朗运动、随机徘徊等等。
贝叶斯方法代考
贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布，和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型，后验分布可以解析或近似，例如，马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要，包括点估计，如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外，所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。
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。
statistics-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。
机器学习代写
随着AI的大潮到来，Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比，Machine Learning在其他领域也有着广泛的应用，因此这门学科成为不仅折磨CS专业同学的“小恶魔”，也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多，跨学科范围广，所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题，StudyGate专业导师团队都能为你轻松解决。
多元统计分析代考
基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量
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，工具箱允许您学习和应用专业技术。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。