如果你也在 怎样代写AP统计这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。
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。
statistics-lab™ 为您的留学生涯保驾护航 在代写AP统计方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写AP统计方面经验极为丰富，各种代写AP统计相关的作业也就用不着说。
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|Where Have All the Fireflies Gone?
A feature article in The Wall Street Journal discusses the disappearance of fireflies. In the article, Professor Sara Lewis of Tufts University and other scholars express concern about the decline in the worldwide population of fireflies.
There are a number of possible explanations for the decline, including habitat reduction of woodlands, wetlands, and open fields; pesticides; and pollution. Artificial nighttime lighting might interfere with the Morsecode-like mating ritual of the fireflies. Some chemical companies pay a bounty for fireflies besause the insects contain two rare shemicals used in medical research and electronic detection systems used in spacecraft.
What does any of this have to do with statistics?
The truth, at this time, is that no one really knows (a) how much the world firefly population has declined or (b) how to explain the decline. The population of all fireflies is simply too large to study in its entirety.
In any study of fireflies, we must rely on incomplete information from samples. Furthermore, from these samples we must draw realistic conclusions that have statistical integrity. This is the kind of work that makes use of statistical methods to determine ways to collect, analyze, and investigate data.
Suppose you are conducting a study to compare firefly populations exposed to normal daylighu/darkness conditions with firefly populations exposed to continuous light ( 24 hours a day). You set up two firefly colonies in a laboratory environment. The two colonies are identical except
Adaptec that one colony is exposed to normal daylight/darkness conditions and the other is exposed to continuous light. Each colony is populated with the same number of mature fireflies. After 72 hours, you count the number of living fireflies in each colony.
After completing this chapter, you will be able to answer the following questions.
(a) Is this an experiment or an observation study? Explain.
(b) Is there a control group? Is there a treatment group?
(c) What is the variable in this study?
(d) What is the level of measurement (nominal, interval, ordinal, or ratio) of the variable?
(See Problem 11 of the Chapter 1 Review Problems.)
统计代写|AP统计作业代写代考|Introduction
Decision making is an important aspect of our lives. We make decisions based on the information we have, our attitudes, and our values. Statistical methods help us examine information. Moreover, statistics can be used for making decisions when we are faced with uncertainties. For instance, if we wish to estimate the proportion of people who will have a severe reaction to a flu shot without giving the shot to everyone who wants it, statistics provides appropriate methods. Statistical methods enable us to look at information from a small collection of people or items and make inferences about a larger collection of people or items.
Procedures for analyzing data, together with rules of inference, are central topics in the study of statistics.The subject of statistics is multifaceted. The following definition of statistics is found in the International Fancyrlopedia of Statistiral Srience, edited hy Mindrag Lovric. Professor David Hand of Imperial College London-the president of the Koyal Statıstical Society – presents the detinition in his article uStatıstics: An Dverview.”
The statistical procedures you will learn in this book should supplement your built-in system of inference-that is, the results from statistical procedures and good sense should dovetail. Of course, statistical methods themselves have no power to work miracles. These methods can help us make some decisions, but not all conceivable decisions. Remember, even a properly applied statistical procedure is no more accurate than the data, or facts, on which it is based. Finally, statistical results should be interpreted by one who understands not only the methods, but also the subject matter to which they have been applied.
The general prerequisite for statistical decision making is the gathering of data. First, we need to identify the individuals or objects to be included in the study and the characteristics or features of the individuals that are of interest.
统计代写|AP统计作业代写代考|Using Basic Terminology
The Hawaii Department of Tropical Agriculture is conducting a study of ready-toharvest pineapples in an experimental field.
(a) The pineapples are the objects (individuals) of the study. If the researchers are interested in the individual weights of pineapples in the field, then the variable consists of weights. At this point, it is important to specify units of measurement
and degrees of accuracy of measurement. The weights could be measured to the nearest ounce or gram. Weight is a quantitative variable because it is a numerical measure. If weights of all the ready-to-harvest pineapples in the field are included in the data, then we have a population. The average weight of all ready-to-harvest pineapples in the field is a parameter:
(b) Suppose the researchers also want data on taste. A panel of tasters rates the pineapples according to the categories “poor,” “acceptable,” and “good.” Only some of the pineapples are included in the taste test. In this case, the variable is taste. This is a qualitative or categorical variable. Because only some of the pineapples in the field are included in the study, we have a sample. The proportion of pineapples in the sample with a taste rating of “good” is a statistic.
Throughout this text, you will encounter guided exercises embedded in the reading material. These exercises are included to give you an opportunity to work immediately with new ideas. The questions guide you through appropriate analysis. Cover the answers on the right side (an index card will fit this purpose). After you have thought about or written down your own response, check the answers. If there are several parts to an exercise, check each part before you continue. You should be able to answer most of these exercise questions, but don’t skip themthey are important.
AP统计代写
统计代写|AP统计作业代写代考|Where Have All the Fireflies Gone?
《华尔街日报》的一篇专题文章讨论了萤火虫的消失。在文章中，塔夫茨大学的 Sara Lewis 教授和其他学者对全球萤火虫数量的下降表示担忧。
下降有多种可能的解释，包括林地、湿地和开阔地的栖息地减少；杀虫剂；和污染。人工夜间照明可能会干扰萤火虫类似摩尔斯电码的交配仪式。一些化学公司为萤火虫支付了赏金，因为萤火虫含有两种用于医学研究的稀有化学物质和用于航天器的电子检测系统。
这与统计数据有什么关系？
在这个时候，事实是没有人真正知道（a）世界萤火虫数量下降了多少，或者（b）如何解释这种下降。所有萤火虫的数量都太大而无法全部研究。
在对萤火虫的任何研究中，我们都必须依赖样本中的不完整信息。此外，我们必须从这些样本中得出具有统计完整性的现实结论。这是一种利用统计方法来确定收集、分析和调查数据的方法的工作。
假设您正在进行一项研究，以比较暴露于正常日光/黑暗条件下的萤火虫种群与暴露于连续光照（一天 24 小时）的萤火虫种群。您在实验室环境中建立了两个萤火虫群落。这两个菌落是相同的，除了
Adaptec 一个菌落暴露在正常的日光/黑暗条件下，而另一个菌落暴露在连续光照下。每个殖民地都居住着相同数量的成熟萤火虫。72 小时后，您计算每个菌落中活萤火虫的数量。
完成本章后，您将能够回答以下问题。
(a) 这是一项实验还是一项观察研究？解释。
(b) 有对照组吗？有治疗组吗？
(c) 本研究中的变量是什么？
(d) 变量的测量水平（名义、区间、序数或比率）是什么？
（见第 1 章复习题的第 11 题。）
统计代写|AP统计作业代写代考|Introduction
决策是我们生活的一个重要方面。我们根据我们掌握的信息、我们的态度和我们的价值观做出决定。统计方法帮助我们检查信息。此外，当我们面临不确定性时，统计数据可用于做出决策。例如，如果我们想估计对流感疫苗有严重反应的人的比例，而不是把疫苗给所有想要的人，统计数据提供了适当的方法。统计方法使我们能够查看来自一小部分人或物品的信息，并对更大的人或物品集合进行推断。
分析数据的程序以及推理规则是统计学研究的核心主题。统计学的主题是多方面的。以下统计定义见于国际统计科学幻想百科全书，由 Mindrag Lovric 编辑。伦敦帝国理工学院的大卫·汉德教授——Koyal Statıstical Society 的主席——在他的文章 uStatıstics: An Dverview 中提出了这一定义。”
你将在本书中学习的统计程序应该补充你的内置推理系统——也就是说，统计程序的结果应该与良好的意识相吻合。当然，统计方法本身并没有创造奇迹的力量。这些方法可以帮助我们做出一些决定，但不是所有可以想象的决定。请记住，即使是正确应用的统计程序也不比它所基于的数据或事实更准确。最后，统计结果应该由不仅了解方法而且了解它们所应用的主题的人来解释。
统计决策的一般先决条件是收集数据。首先，我们需要确定要纳入研究的个人或对象以及感兴趣的个人的特征或特征。
统计代写|AP统计作业代写代考|Using Basic Terminology
夏威夷热带农业部正在试验田中对即收即收的菠萝进行研究。
(a) 菠萝是研究的对象（个体）。如果研究人员对田间菠萝的个体重量感兴趣，那么变量由权重组成。在这一点上，指定测量单位和测量
准确度是很重要的。重量可以测量到最接近的盎司或克。重量是一个定量变量，因为它是一个数值度量。如果数据中包含田间所有待采菠萝的重量，那么我们就有了一个种群。田间所有待采菠萝的平均重量是一个参数：
(b) 假设研究人员还想要有关口味的数据。一组品尝者根据“差”、“可接受”和“好”三个等级对菠萝进行评分。只有一些菠萝被包括在口味测试中。在这种情况下，变量是味道。这是一个定性或分类变量。因为研究中只包括了该领域的一些菠萝，所以我们有一个样本。样品中的菠萝比例为“好”是一个统计数据。
在整本书中，您将遇到嵌入在阅读材料中的指导练习。包括这些练习是为了让您有机会立即处理新想法。这些问题将引导您进行适当的分析。盖住右侧的答案（索引卡适合此目的）。在您考虑或写下自己的回答后，检查答案。如果一个练习有多个部分，请在继续之前检查每个部分。您应该能够回答大多数这些练习题，但不要跳过它们，它们很重要。
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。