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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|How Often Do Lie Detectors Lie?
James Burke is an educator who is known for his interesting science-related radio and television shows aired by the British Broadcasting Corporation. His book Chances: Risk and Odds in Everyday Life (Virginia Books, London) contains a great wealth of fascinating information about probabilities. The following quote is from Professor Burke’s book:
If I take a polygraph test and lie, what is the risk I will be detected? According to some studies, there’s about a 72 percent chance you will be caught by the machine.
What is the risk that if I take a polygraph test it will incorrectly say that $I$ lied? At least 1 in 15 will be thus falsely accused.
Both of these statements contain conditional probabilities, which we will study in Section 4.2. Information from that section will enable us to answer the following:
Suppose a person answers $10 \%$ of a long battery of questions with lies. Assume that the remaining $90 \%$ of the questions are answered truthfully.
- Estimate the percentage of answers the polygraph will wrongly indicate as lies.
- Estimate the percentage of answers the polygraph will correctly indicate as lies.
If the polygraph indicated that $30 \%$ of the questions were answered as lies, what would you estimate for the actual percentage of questions the person answered as lies? (See Problems 27 and 28 in Section 4.2.)
统计代写|AP统计作业代写代考|Probability Assignment
Consider each of the following events, and determine how the probability is assigned.
(a) A sports announcer claims that Sheila has a $90 \%$ chance of breaking the world record in the 100-yard dash.
SOLUTION: It is likely the sports announcer used intuition based on Sheila’s past performance.
Section 4.1 What is Probability? 145
(b) Henry figures that if he guesses on a true-false question, the probability of getting it right is $0.50$.
sOLUTION: In this case there are two possible outcomes: Henry’s answer is either correct or incorrect. Since Henry is guessing, we assume the outcomes are equally likely. There are $n=2$ equally likely outcomes, and only one is correct. By formula (2),
$$
P(\text { correct answer })=\frac{\text { Number of favorable outcomes }}{\text { Total number of outcomes }}=\frac{1}{2}=0.50
$$
(c) The Right to Health lobby claims that the probability of getting an erroneous medical laboratory report is $0.40$, based on a random sample of 200 laboratory reports, of which 80 were erroneous.
soLUTION: Formula (1) for relative frequency gives the probability, with sample size $n=200$ and number of errors $f=80$.
$$
P(\text { error })=\text { relative frequency }=\frac{f}{n}=\frac{80}{200}=0.40
$$
统计代写|AP统计作业代写代考|Using a Sample Space
Human eye color is controlled by a single pair of genes (one from the father and one from the mother) called a genotype. Brown eye color, $\mathrm{B}$, is dominant over blue eye color, $\ell$. Therefore, in the genotype $\mathrm{B} \ell$, consisting of one brown gene $\mathrm{B}$ and one blue gene $\ell$, the brown gene dominates. A person with a $\mathrm{B} \ell$ genotype has brown eyes.
If both parents have brown eyes and have genotype $\mathrm{B} \ell$, what is the probability that their child will have blue eyes? What is the probability the child will have brown eyes?
SOLUTION: To answer these questions, we need to look at the sample space of all possible eye-color genotypes for the child. They are given in Table 4-1.
TABLE 4-1 Eye Color Genotypes for Child
\begin{tabular}{lll}
\hline & \multicolumn{2}{c}{ Mother } \
\cline { 2 – 3 } Father & B & $\ell$ \
\hline B & BB & $B \ell$ \
$\ell$ & $\ell B$ & $\ell \ell$ \
\hline
\end{tabular}
According to genetics theory, the four possible genotypes for the child are equally likely. Therefore, we can use Formula (2) to compute probabilities. Blue eyes can occur only with the $\ell \ell$ genotype, so there is only one outcome favorable to blue eyes. By formula (2),
$$
P(\text { blue eyes })=\frac{\text { Number of favorable outcomes }}{\text { Total number of outcomes }}=\frac{1}{4}
$$
Brown eyes occur with the three remaining genotypes: $B B, B \ell$, and $\ell B$. By formula (2),
$$
P(\text { brown eyes })=\frac{\text { Number of favorable outcomes }}{\text { Total number of outcomes }}=\frac{3}{4}
$$
There is another important point about probability assignments of simple events.
The sum of the probabilities of all simple events in a sample space must equal $1 .$
We can use this fact to determine the probability that an event will not occur. For instance, if you think the probability is $0.65$ that you will win a tennis match, you assume the probability is $0.35$ that your opponent will win.
The complement of an event $A$ is the event that $A$ does not occur. We use the notation $A^{c}$ to designate the complement of event $A$. Figure 41 shows the event $A$ and its complement $A^{c}$. In the literature, the symbols $A^{\circ}$ and $\bar{A}$ are also used to designate the complement of event $A$
Noticéc that the two distinet events $A$ ând $A^{2}$ make up the entire samplec spăce. Therefore, the sum of their probabilities is 1 .
The complement of event $A$ is the event that $A$ does not occur. $A^{c}$ designates the complement of event $A$. Furthermore,
- $P(A)+P\left(A^{c}\right)=1$
- $P($ event $A$ does not occur $)=P\left(A^{c}\right)=1-P(A)$
AP统计代写
统计代写|AP统计作业代写代考|How Often Do Lie Detectors Lie?
詹姆斯·伯克 (James Burke) 是一位教育家，以英国广播公司播出的有趣的与科学相关的广播和电视节目而闻名。他的书《机会：日常生活中的风险和赔率》（弗吉尼亚图书，伦敦）包含大量关于概率的有趣信息。以下引用来自伯克教授的书：
如果我进行测谎测试并撒谎，我将被检测到的风险是什么？根据一些研究，你被机器抓住的几率约为 72%。
如果我进行测谎测试，它会错误地说出有什么风险？一世撒谎？因此，至少每 15 人中就有 1 人会被诬告。
这两个陈述都包含条件概率，我们将在 4.2 节中研究。该部分的信息将使我们能够回答以下问题：
假设有人回答10%一长串带有谎言的问题。假设剩余90%的问题得到如实回答。
- 估计测谎仪将错误地指示为谎言的答案的百分比。
- 估计测谎仪将正确指示为谎言的答案百分比。
如果测谎仪表明30%的问题被回答为谎言，你会估计这个人回答为谎言的问题的实际百分比是多少？（见第 4.2 节中的问题 27 和 28。）
统计代写|AP统计作业代写代考|Probability Assignment
考虑以下每个事件，并确定如何分配概率。
(a) 一位体育播音员声称希拉有一个90%打破100码短跑世界纪录的机会。
解决方案：很可能体育播音员根据希拉过去的表现使用了直觉。
4.1 什么是概率？145
(b) 亨利计算出，如果他猜测一个真假问题，猜对的概率是0.50.
解决方案：在这种情况下，有两种可能的结果：亨利的答案是正确的或不正确的。由于亨利在猜测，我们假设结果的可能性相同。有n=2同样可能的结果，只有一个是正确的。由式（2），
磷( 正确答案 )= 有利结果的数量 结果总数 =12=0.50
(c) 健康权游说团体声称，获得错误医学实验室报告的可能性是0.40，基于 200 份实验室报告的随机样本，其中 80 份是错误的。
解决方案：相对频率的公式（1）给出概率，样本大小n=200和错误数量F=80.
磷( 错误 )= 相对频率 =Fn=80200=0.40
统计代写|AP统计作业代写代考|Using a Sample Space
人眼的颜色由一对称为基因型的基因（一个来自父亲，一个来自母亲）控制。棕色的眼睛颜色，乙，比蓝眼睛颜色占主导地位，ℓ. 因此，在基因型乙ℓ, 由一个棕色基因组成乙和一个蓝色基因ℓ，棕色基因占主导地位。一个人乙ℓ基因型有棕色的眼睛。
如果父母双方都有棕色的眼睛并且有基因型乙ℓ，他们的孩子有蓝眼睛的概率是多少？孩子有棕色眼睛的概率是多少？
解决方案：要回答这些问题，我们需要查看孩子所有可能的眼睛颜色基因型的样本空间。它们在表 4-1 中给出。
表 4-1 儿童眼睛颜色基因型
\begin{tabular}{lll} \hline & \multicolumn{2}{c}{ Mother } \ \cline { 2 – 3 } 父亲 & B & $\ell$ \ \hline B & BB & $B \ell$ \ $\ell$ & $\ell B$ & $\ell \ell$ \ \hline \end{表格}\begin{tabular}{lll} \hline & \multicolumn{2}{c}{ Mother } \ \cline { 2 – 3 } 父亲 & B & $\ell$ \ \hline B & BB & $B \ell$ \ $\ell$ & $\ell B$ & $\ell \ell$ \ \hline \end{表格}
根据遗传学理论，孩子的四种可能基因型的可能性相同。因此，我们可以使用公式（2）来计算概率。蓝眼睛只能发生在ℓℓ基因型，所以只有一种结果有利于蓝眼睛。由式（2），
磷( 蓝眼睛 )= 有利结果的数量 结果总数 =14
剩下的三种基因型出现棕色眼睛：乙乙,乙ℓ， 和ℓ乙. 由式（2），
磷( 棕色的眼睛 )= 有利结果的数量 结果总数 =34
关于简单事件的概率分配还有一个重要的点。
样本空间中所有简单事件的概率之和必须等于1.
我们可以使用这个事实来确定事件不会发生的概率。例如，如果你认为概率是0.65你会赢得一场网球比赛，你假设概率是0.35你的对手会赢。
事件的补充一种是事件一种不会发生。我们使用符号一种C指定事件的补充一种. 图 41 显示了事件一种及其补语一种C. 在文学作品中，符号一种∘和一种¯也用于指定事件的补充一种
注意这两个不同的事件一种母鸡一种2组成整个采样空间。因此，它们的概率之和为 1 。
事件的补充一种是事件一种不会发生。一种C指定事件的补充一种. 此外，
- 磷(一种)+磷(一种C)=1
- 磷(事件一种不发生)=磷(一种C)=1−磷(一种)
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。