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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|Simple Random Samples
Eat lamb-20,000 coyotes can’t be wrong!
This slogan is sometimes found on bumper stickers in the western United States. The slogan indicates the trouble that ranchers have experienced in protecting their flocks from predators. Based on their experience with this sample of the coyote population, the ranchers concluded that all coyotes are dangerous to their flocks and should be eliminated! The ranchers used a special poison bait to get rid of the coyotes. Not only was this poison distributed on ranch land, but with government cooperation, it also was distributed widely on public lands.
The ranchers found that the results of the widespread poisoning were not very beneficial. The sheep-eating coyotes continued to thrive while the general population of coyotes and other predators declined. What was the problem? The sheep-eating coyotes that the ranchers had observed were not a representative sample of all coyotes. Modern methods of predator control, however, target the sheep-eating coyotes. To a certain extent, the new methods have come about through a closer examination of the sampling techniques used.
In this section, we will examine several widely used sampling techniques. One of the most important sampling techniques is a simple random sample.
In a simple random sample, not only does every sample of the specified size have an equal chance of being selected, but every individual of the population also has an equal chance of being selected. However, the fact that each individual has an equal chance of being selected does not necessarily imply a simple random sample. Remember, for a simple random sample, every sample of the given size must also have an equal chance of being selected.
统计代写|AP统计作业代写代考|Random-Number Table
Use a random-number table to pick a random sample of 30 cars from a population of 500 cars.
SOLUTION: Again, we assign each car a different number between 1 and 500 , inclusive. Then we use the random-number table to choose the sample. Table 1 in Appendix II has 50 rows and 10 blocks of five digits each; it can be thought of as a solid mass of digits that has been broken up into rows and blocks for user convenience.
You read the digits by beginning anywhere in the table. We dropped a pin on the table, and the head of the pin landed in row 15, block 5 . We’ll begin there and list all the digits in that row. If we need more digits, we’ll move on to row 16 , and so on. The digits we begin with are
$\begin{array}{llllll}99281 & 59640 & 15221 & 96079 & 09961 & 05371\end{array}$
Since the highest number assigned to a car is 500 , and this number has three digits, we regroup our digits into blocks of 3 :
$\begin{array}{llllllllll}992 & 815 & 964 & 015 & 221 & 960 & 790 & 996 & 105 & 371\end{array}$
To construct our random sample, we use the first 30 car numbers we encounter in the random-number table when we start at row 15, block 5 . We skip the first three groups- 992,815 , and 964 -because these numbers are all too large. The next group of three digits is 015 , which corresponds to 15 . Car number 15 is the first car included in our sample, and the next is car number 221 . We skip the next three groups and then include car numbers 105 and 371 . To get the rest of the cars in the sample, we continue to the next line and use the random-number table in the same fashion. If we encounter a number we’ve used before, we skip it.
统计代写|AP统计作业代写代考|Other Sampling Techniques
Although we will assume throughout this text that (simple) random samples are used, other methods of sampling are also widely used. Appropriate statistical techniques exist for these sampling methods, but they are beyond the scope of this text.
One of these sampling methods is called stratified sampling. Groups or classes inside a population that share a common characteristic are called strata (plural of stratum). For example, in the population of all undergraduate college students, some strata might be freshmen, sophomores, juniors, or seniors. Other strata might be men or women, in-state students or out-of-state students, and so on. In the method of stratified sampling, the population is divided into at least two distinct strata. Then a (simple) random sample of a certain size is drawn from each stratum, and the information obtained is carefully adjusted or weighted in all resulting calculations.
The groups or strata are often sampled in proportion to their actual percentages of occurrence in the overall population. However, other (more sophisticated) ways to determine the optimal sample size in each stratum may give the best results. In general, statistical analysis and tests based on data obtained from stratified samples are somewhat different from techniques discussed in an introductory course in statistics. Such methods for stratified sampling will not be discussed in this text.
Another popular method of sampling is called systematic sampling. In this method, it is assumed that the elements of the population are arranged in some natural sequential order. Then we select a (random) starting point and select every $k$ th element for our sample. For example, people lining up to buy rock concert tickets are “in order.” To generate a systematic sample of these people (and ask questions regarding topics such as age, smoking habits, income level, etc.), we could include every fifth person in line. The “starting” person is selected at random from the first five.
The advantage of a systematic sample is that it is easy to get. However, there are dangers in using systematic sampling. When the population is repetitive or cyclic in nature, systematic sampling should not be used. For example, consider a fabric mill that produces dress material. Suppose the loom that produces the material makes a mistake every 17th yard, but we check only every 16 th yard with an automated electronic scanner. In this case, a random starting point may or may not result in detection of fabric flaws before a large amount of fabric is produced.
Cluster sampling is a method used extensively by government agencies and certain private research organizations. In cluster sampling, we begin by dividing the demographic area into sections. Then we randomly select sections or clusters. Every member of the cluster is included in the sample. For example, in conducting a survey of school children in a large city, we could first randomly select five schools and then include all the children from each selected school.
Often a population is very large or geographically spread out. In such cases, samples are constructed through a multistage sample design of several stages, with the final stage consisting of clusters. For instance, the government Current Population Survey interviews about 60,000 households across the United States each month by means of a multistage sample design.
For the Current Population Survey, the first stage consists of selecting samples of large geographic areas that do not cross state lines. These areas are further broken down into smaller blocks, which are stratified according to ethnic and other factors. Stratified samples of the blocks are then taken. Finally, housing units in each chosen block are broken into clusters of nearby housing units. A random sample of these clusters of housing units is selected, and each household in the final cluster is interviewed. Convenience sampling simply uses results or data that are conveniently and readily obtained. In some cases, this may be all that is available, and in many cases, it is better than no information at all. However, convenience sampling does run the risk of being severely biased. For instance, consider a newsperson who wishes to get the “opinions of the people” about a proposed seat tax to be imposed on tickets to all sporting events. The revenues from the seat tax will then be used to support the local symphony. The newsperson stands in front of a concert hall and surveys the first five people exiting after a symphony performance who will cooperate. This method of choosing a sample will produce some opinions, and perhaps some human interest stories, but it certainly has bias. It is hoped that the city council will not use these opinions as the sole basis for a decision about the proposed tax. It is good advice to be very cautious indeed when the data come from the method of convenience sampling.
AP统计代写
统计代写|AP统计作业代写代考|Simple Random Samples
吃羊肉——两万只小狼不会错！
这个标语有时出现在美国西部的保险杠贴纸上。这个口号表明了牧场主在保护他们的羊群免受掠食者侵害方面遇到的麻烦。根据他们对土狼种群样本的经验，牧场主得出结论，所有土狼对他们的羊群都是危险的，应该被消灭！牧场主使用一种特殊的毒饵来摆脱土狼。这种毒药不仅分布在牧场的土地上，而且在政府的配合下，也在公共土地上广泛分布。
牧场主们发现，普遍中毒的结果并不是很有益。以羊为食的土狼继续茁壮成长，而土狼和其他捕食者的总体数量下降。出了什么问题？牧场主观察到的吃羊的土狼并不是所有土狼的代表性样本。然而，现代控制捕食者的方法针对的是吃羊的土狼。在一定程度上，新方法是通过对所使用的抽样技术进行更仔细的检查而产生的。
在本节中，我们将研究几种广泛使用的抽样技术。最重要的抽样技术之一是简单的随机抽样。
在一个简单的随机样本中，不仅每个指定大小的样本都有相同的被选中的机会，而且总体中的每个个体也有相同的被选中的机会。然而，每个人被选中的机会均等这一事实并不一定意味着一个简单的随机样本。请记住，对于一个简单的随机样本，给定大小的每个样本也必须有相同的机会被选中。
统计代写|AP统计作业代写代考|Random-Number Table
使用随机数表从 500 辆汽车中随机抽取 30 辆汽车作为样本。
解决方案：同样，我们为每辆车分配一个介于 1 和 500 之间的不同编号，包括 1 和 500 之间。然后我们使用随机数表来选择样本。附录二中的表1有50行10块，每块5位；它可以被认为是一大堆数字，为了方便用户，它们被分成行和块。
您可以从表格中的任何位置开始读取数字。我们在桌子上丢了一个大头针，大头针落在第 15 行，第 5 块。我们将从那里开始并列出该行中的所有数字。如果我们需要更多数字，我们将转到第 16 行，依此类推。我们开始的数字是
992815964015221960790996105371
由于分配给汽车的最高数字是 500 ，并且这个数字有三位，我们将我们的数字重新组合为 3 块：
992815964015221960790996105371
为了构建我们的随机样本，我们使用从第 15 行第 5 块开始时在随机数表中遇到的前 30 个车号。我们跳过前三组——992,815 和 964——因为这些数字都太大了。下一组三位数字是 015 ，对应于 15 。15 号车是我们样本中包含的第一辆车，接下来是 221 号车。我们跳过接下来的三组，然后包括车号 105 和 371。为了得到样本中的其余汽车，我们继续下一行并以相同的方式使用随机数表。如果我们遇到以前使用过的数字，我们会跳过它。
统计代写|AP统计作业代写代考|Other Sampling Techniques
尽管我们将在本文中假设使用（简单）随机样本，但其他抽样方法也被广泛使用。这些抽样方法存在适当的统计技术，但它们超出了本文的范围。
其中一种抽样方法称为分层抽样。群体中具有共同特征的群体或类别称为阶层（阶层的复数）。例如，在所有本科生的人口中，某些阶层可能是大一、大二、大三或大四。其他阶层可能是男性或女性、州内学生或州外学生等。在分层抽样的方法中，人口被分成至少两个不同的阶层。然后从每个层中抽取一定大小的（简单）随机样本，并在所有结果计算中仔细调整或加权获得的信息。
群体或阶层通常按其在总人口中的实际发生百分比按比例进行抽样。但是，确定每个层中的最佳样本量的其他（更复杂的）方法可能会产生最佳结果。一般来说，基于从分层样本中获得的数据的统计分析和测试与统计学入门课程中讨论的技术有些不同。本文将不讨论这种分层抽样的方法。
另一种流行的抽样方法称为系统抽样。在这种方法中，假设总体的元素以某种自然的顺序排列。然后我们选择一个（随机）起点并选择每个到我们样本的第一个元素。例如，排队购买摇滚音乐会门票的人是“有秩序的”。为了生成这些人的系统样本（并询问有关年龄、吸烟习惯、收入水平等主题的问题），我们可以将每五个人排队。“开始”的人是从前五个人中随机选择的。
系统样本的优点是容易获取。然而，使用系统抽样存在危险。当总体具有重复性或循环性时，不应使用系统抽样。例如，考虑一家生产服装面料的纺织厂。假设生产材料的织机每隔 17 码就会出错一次，但我们只用自动电子扫描仪每 16 码检查一次。在这种情况下，随机起点可能会或可能不会导致在生产大量织物之前检测到织物缺陷。
整群抽样是政府机构和某些私人研究组织广泛使用的一种方法。在整群抽样中，我们首先将人口统计区域划分为多个部分。然后我们随机选择部分或集群。集群的每个成员都包含在样本中。例如，在对一个大城市的学童进行调查时，我们可以先随机选择五所学校，然后将每所选定学校的所有孩子都包括在内。
人口通常非常庞大或地理上分散。在这种情况下，样本是通过多个阶段的多阶段样本设计构建的，最后阶段由集群组成。例如，政府当前人口调查通过多阶段样本设计每月访问美国各地约 60,000 个家庭。
对于当前人口调查，第一阶段包括选择不跨越州界的大地理区域的样本。这些区域进一步细分为更小的区块，并根据种族和其他因素进行分层。然后获取块的分层样本。最后，每个选定街区的住房单元被分成附近的住房单元群。从这些住房单元集群中随机抽取样本，并对最终集群中的每个家庭进行访谈。方便抽样只是使用方便且容易获得的结果或数据。在某些情况下，这可能是所有可用的信息，在许多情况下，这总比没有信息要好。然而，便利抽样确实存在严重偏差的风险。例如，考虑一位新闻工作者，他希望就提议对所有体育赛事门票征收的座位税征求“人民的意见”。席位税的收入将用于支持当地的交响乐团。新闻工作者站在音乐厅前，调查在交响乐表演后离开的前五名愿意合作的人。这种选择样本的方法会产生一些意见，也许还会产生一些人类感兴趣的故事，但它肯定有偏见。希望市议会不要将这些意见作为决定拟议税收的唯一依据。当数据来自方便抽样的方法时，确实非常谨慎是一个很好的建议。席位税的收入将用于支持当地的交响乐团。新闻工作者站在音乐厅前，调查在交响乐表演后离开的前五名愿意合作的人。这种选择样本的方法会产生一些意见，也许还会产生一些人类感兴趣的故事，但它肯定有偏见。希望市议会不要将这些意见作为决定拟议税收的唯一依据。当数据来自方便抽样的方法时，确实非常谨慎是一个很好的建议。席位税的收入将用于支持当地的交响乐团。新闻工作者站在音乐厅前，调查在交响乐表演后离开的前五名愿意合作的人。这种选择样本的方法会产生一些意见，也许还会产生一些人类感兴趣的故事，但它肯定有偏见。希望市议会不要将这些意见作为决定拟议税收的唯一依据。当数据来自方便抽样的方法时，确实非常谨慎是一个很好的建议。但它肯定有偏见。希望市议会不要将这些意见作为决定拟议税收的唯一依据。当数据来自方便抽样的方法时，确实非常谨慎是一个很好的建议。但它肯定有偏见。希望市议会不要将这些意见作为决定拟议税收的唯一依据。当数据来自方便抽样的方法时，确实非常谨慎是一个很好的建议。
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。