AP统计代写

分类： AP统计代写

统计代写|AP统计作业代写代考|Stem-and-Leaf Displays

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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|Exploratory Data Analysis

Together with histograms and other graphics techniques, the stem-and-leaf display is one of many useful ways of studying data in a field called exploratory data analysis (often abbreviated as EDA). John W. Tukey wrote one of the definitive books on the subject, Exploratory Data Analysis (Addison-Wesley). Another very useful reference for EDA techniques is the book Applications, Basics, and Computing of Exploratory Data Analysis by Paul F. Velleman and David C. Hoaglin (Duxbury Press). Exploratory data analysis techniques are particularly useful for detecting patterns and extreme data values. They are designed to help us explore a data set, to ask questions we had not thought of before, or to pursue leads in many directions.

EDA techniques are similar to those of an explorer. An explorer has a general idea of destination but is always alert for the unexpected. An explorer needs to assess situations quickly and often simplify and clarify them. An explorer makes pictures-that is, maps showing the relationships of landscape features. The aspects of rapid implementation,

visual displays such as graphs and charts, data simplification, and robustness (that is, analysis that is not influenced much by extreme data values) are key ingredients of EDA techniques. In addition, these techniques are good for exploration because they require very few prior assumptions about the data.

EDA methods are especially useful when our data have been gathered for general interest and observation of subjects. For example, we may have data regarding the ages of applicants to graduate programs. We don’t have a specific question in mind. We want to see what the data reveal. Are the ages fairly uniform or spread out? Are thers exesptionally young or old applicants? If there arc, we might look at other characteristics of these applicants, such as field of study. EDA methods help us quickly absorb some aspects of the data and then may lead us to ask specific questions to which we might apply methods of traditional statistics.

In contrast, when we design an experiment to produce data to answer a specific question, we focus on particular aspects of the data that are useful to us. If we want to determine the average highway gas mileage of a specific sports car, we use that model car in well-designed tests. We don’t need to worry about unexpected road conditions, poorly trained drivers, different fuel grades, sudden stops and starts, etc. Our experiment is designed to control outside factors. Consequently, we do not need to “explore” our data as much. We can often make valid assumptions about the data. Methods of traditional statistics will be very useful to analyze such data and answer our specific questions.

统计代写|AP统计作业代写代考|Stem-and-Leaf Display

Many airline passengers seem weighted down by their carry-on luggage. Just how much weight are they carrying? The carry-on luggage weights in pounds for a random sample of 40 passengers returning from a vacation to Hawaii were recorded (see Table 2-15).

To make a stem-and-leaf display, we break the digits of each data value into two parts. The left group of digits is called a stem, and the remaining group of digits on the right is called a leaf. We are free to choose the number of digits to be included in the stem.

The weights in our example consist of two-digit numbers. For a two-digit number, the stem selection is obviously the left digit. In our case, the tens digits will form the stems, and the units digits will form the leaves. For example, for the weight 12 , the stem is 1 and the leaf is 2 . For the weight 18 , the stem is again 1, but the leaf is 8. In the stem-and-leaf display, we list each possible stem once on the left and all its leaves in the same row on the right, as in Figure 2-15(a). Finally, we order the leaves as shown in Figure 2- $15(b)$.

Figure 2-15 shows a stem-and-leaf display for the weights of carry-on luggage. From the stem-and-leaf display in Figure 2-15, we see that two bags weighed $27 \mathrm{lb}$, one weighed $3 \mathrm{lb}$, one weighed $51 \mathrm{lb}$, and so on. We see that most of the weights were in the 30 -lb range, only two were less than $10 \mathrm{lb}$, and six were over $40 \mathrm{lb}$. Note that the lengths of the lines containing the leaves give the visual impression that a sideways histogram would present.

As a final step, we need to indicate the scale. This is usually done by indicating the value represented by a stem and one leaf.

There are no firm rules for selecting the group of digits for the stem. But whichever group you select, you must list all the possible stems from smallest to largest in the data collection.

统计代写|AP统计作业代写代考|Cowboys: Longevity

Cowboys: Longevity How long did real cowboys live? One answer may be found in the book The Last Cowboys by Connie Brooks (University of New Mexico Press). This delightful book presents a thoughtful sociological study of cowboys in west Texas and southeastem New Mexico around the year 1890. A sample of 32 cowboys gave the following years of longevity:

(a) Make a stem-and-leaf display for these data.
(b) Interpretation Consider the following quote from Baron von Richthofen in his Cattle Raising on the Plains of North America: “Cowboys are to be found among the sons of the best families. The truth is probably that most were not a drunken, gambling lot, quick to draw and fire their pistols.”
Does the data distribution of longevity lend credence to this quote?
Ecology: Habitat Wetlands offer a diversity of benefits. They provide a habitat for wildlife, spawning grounds for U.S. commercial fish, and renewable timber resources. In the last 200 years, the United States has lost more than half its wetlands. Environmental Almanac gives the percentage of wetlands lost in each state in the last 200 years. For the lower 48 states, the percentage loss of wetlands per state is as follows:
$\begin{array}{lllcllllll}46 & 37 & 36 & 42 & 81 & 20 & 73 & 59 & 35 & 50 \ 87 & 52 & 24 & 27 & 38 & 56 & 39 & 74 & 56 & 31 \ 27 & 91 & 46 & 9 & 54 & 52 & 30 & 33 & 28 & 35 \ 35 & 23 & 90 & 72 & 85 & 42 & 59 & 50 & 49 & \ 48 & 38 & 60 & 46 & 87 & 50 & 89 & 49 & 67 & \end{array}$
Make a stem-and-leaf display of these data. Be sure to indicate the scale. How are the percentages distributed? Is the distribution skewed? Are there any gaps?

统计代写|AP统计作业代写代考|Exploratory Data Analysis

与直方图和其他图形技术一起，茎叶显示是在称为探索性数据分析（通常缩写为 EDA）的领域中研究数据的许多有用方法之一。John W. Tukey 写了一本关于该主题的权威书籍，探索性数据分析 (Addison-Wesley)。另一个对 EDA 技术非常有用的参考资料是 Paul F. Velleman 和 David C. Hoaglin（Duxbury 出版社）所著的《探索性数据分析的应用、基础和计算》一书。探索性数据分析技术对于检测模式和极端数据值特别有用。它们旨在帮助我们探索数据集，提出我们以前没有想到的问题，或者在多个方向上寻找线索。

EDA 技术类似于资源管理器的技术。探险家对目的地有一个大致的了解，但总是对意外情况保持警惕。探索者需要快速评估情况，并经常简化和澄清它们。探险家制作图片——即显示景观特征关系的地图。快速实施方面，

图形和图表等可视化显示、数据简化和稳健性（即不受极端数据值影响的分析）是 EDA 技术的关键要素。此外，这些技术非常适合探索，因为它们只需要很少的关于数据的先验假设。

EDA 方法在我们为一般兴趣和观察对象而收集数据时特别有用。例如，我们可能有关于研究生课程申请者年龄的数据。我们没有具体的问题。我们想看看数据揭示了什么。年龄是相当统一的还是分散的？他们是特别年轻还是年长的申请人？如果有弧线，我们可能会查看这些申请人的其他特征，例如研究领域。EDA 方法帮助我们快速吸收数据的某些方面，然后可能会引导我们提出可以应用传统统计方法的特定问题。

相比之下，当我们设计一个实验来产生数据来回答特定问题时，我们会关注数据中对我们有用的特定方面。如果我们想确定特定跑车的平均公路油耗，我们会在精心设计的测试中使用该模型车。我们不需要担心意外的路况、训练有素的司机、不同的燃料等级、突然停止和启动等。我们的实验旨在控制外部因素。因此，我们不需要过多地“探索”我们的数据。我们通常可以对数据做出有效的假设。传统统计方法对于分析此类数据并回答我们的具体问题将非常有用。

统计代写|AP统计作业代写代考|Stem-and-Leaf Display

许多航空公司的乘客似乎被他们的随身行李压得喘不过气来。他们承载了多少重量？记录了从假期返回夏威夷的 40 名乘客的随机样本，以磅为单位的随身行李重量（见表 2-15）。

为了进行茎叶显示，我们将每个数据值的数字分成两部分。左边的一组数字称为茎，右边的剩余数字组称为叶。我们可以自由选择要包含在词干中的位数。

我们示例中的权重由两位数组成。对于两位数的数字，词干选择显然是左边的数字。在我们的例子中，十位数字将形成茎，个位数字将形成叶子。例如，对于权重 12，茎为 1，叶为 2。对于权重 18 ，茎再次为 1，但叶为 8。在茎叶显示中，我们在左侧列出每个可能的茎，在右侧列出同一行中的所有叶子，如图2-15(a)。最后，我们对叶子进行排序，如图 2 所示——15(b).

图 2-15 显示了随身行李重量的柱叶式显示。从图 2-15 中的茎叶显示中，我们看到两个袋子称重27一世b, 一个称重3一世b, 一个称重51一世b，等等。我们看到大部分重量都在 30 磅范围内，只有两个重量小于10一世b, 六个结束了40一世b. 请注意，包含叶子的线条的长度给人的视觉印象是会出现横向直方图。

作为最后一步，我们需要指明比例。这通常通过指示由茎和叶表示的值来完成。

选择词干的数字组没有固定的规则。但是无论您选择哪个组，您都必须在数据集合中从小到大列出所有可能的词根。

统计代写|AP统计作业代写代考|Cowboys: Longevity

牛仔：长寿真正的牛仔能活多久？一个答案可以在康妮·布鲁克斯（新墨西哥大学出版社）的《最后的牛仔》一书中找到。这本令人愉快的书对 1890 年左右德克萨斯州西部和新墨西哥州东南部的牛仔进行了深思熟虑的社会学研究。32 名牛仔的样本给出了以下几年的长寿：

(a) 对这些数据进行茎叶显示。
(b) 解释考虑一下冯·李希霍芬男爵在他的《北美平原养牛》中的以下引言：“在最好的家庭的儿子中可以找到牛仔。事实可能是，大多数人都不是醉酒、赌博、快速拔枪和开枪的人。”
长寿的数据分布是否证明了这句话的可信度？
生态：栖息地湿地提供多种益处。它们为野生动物提供栖息地、美国商业鱼类的产卵场和可再生木材资源。在过去的 200 年里，美国失去了一半以上的湿地。环境年鉴给出了过去 200 年中每个州失去的湿地百分比。对于较低的 48 个州，每个州的湿地损失百分比如下：
46373642812073593550 87522427385639745631 2791469545230332835 352390728542595049 483860468750894967
对这些数据进行茎叶显示。一定要标明比例。百分比是如何分配的？分布是否有偏差？有没有差距？

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.

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。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Time-Series Graphs

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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|Bar Graph

Figure 2-11 shows two bar graphs depicting the life expectancies for men and women born in the designated year. Let’s analyze the features of these graphs.

SOLUTION: The graphs are called cluster bar graphs because there are two bars for each year of birth. One bar represents the life expectancy for men, and the other represents the life expectancy for women. The height of each bar represents the life expectancy (in years).

An important feature illustrated in Figure 2-11(b) is that of a changing scale. Notice that the scale between 0 and 65 is compressed. The changing scale amplifies the apparent difference between life spans for men and women, as well as the increase in life spans from those bom in 1980 to the projected span of those born in 2010 .Quality control is an important aspect of today’s production and service industries. Dr. W. Edwards Deming was one of the developers of total quality management (TQM). In his book Out of Crisis, he outlines many strategies for monitoring and improving service and production industries. In particular, Dr. Deming recommends the use of some statistical methods to organize and analyze data from industries so that sources of problems can be identified and then corrected. Pareto (pronounced “Pah-rāy-[ॅ”) charts are among the many techniques used in qualitycontrol programs.

统计代写|AP统计作业代写代考|Time-Series Graph

Suppose you have been in the walking/jogging exercise program for 20 weeks, and for each week you have recorded the distance you covered in 30 minutes. Your data $\log$ is shown in Table 2-14.

(a) Make a time-series graph.
sOLUTION: The data are appropriate for a time-series graph because they represent the same measurement (distance covered in a 30 -minute perind) taken at different times. The measurements are also recorded at equal time intervals (every week). To make our time-series graph, we list the weeks in order on the horizontal scale. Above each week, plot the distance covered that week on the vertical scale. Then connect the dots. Figure $2-14$ shows the time-series graph. Be sure the scales are labeled.

(b) Interpretation From looking at Figure 2-14, can you detect any patterns?
SOLUTION: There seems to be an upward trend in distance covered. The distances covered in the last few weeks are about a mile farther than those for the first few weeks. However, we cannot conclude that this trend will continue. Perhaps you have reached your goal for this training activity and now wish to maintain a distance of about $2.5$ miles in 30 minutes.

Data sets composed of similar measurements taken at regular intervals over time. are called time series. Time series are often used in economics, finance, sociology, medicine, and any other situation in which we want to study or monitor a similar measure over a period of time. A time-series graph can reveal some of the main features of a time series.

统计代写|AP统计作业代写代考|Lifestyle: Hide the Mess!

Lifestyle: Hide the Mess! A survey of 1000 adults (reported in USA Today) uncovered some interesting housekeeping secrets. When unexpected company comes, where do we hide the mess? The survey showed that $68 \%$ of the respondents toss their mess into the closet, $23 \%$ shove things under the bed, $6 \%$ put things into the bathtub, and $3 \%$ put the mess into the freezer. Make a circle graph to display this information.

Education: College Professors’ Time How do college professors spend their time? The National Education Association Almanac of Higher
Education gives the following average distribution of professional time allocation: teaching, $51 \%$; research, $16 \%$; professional growth, $5 \%$; community service, $11 \%$; service to the college, $11 \%$; and consulting outside the college, $6 \%$. Make a pie chart showing the allocation of professional time for college professors.

FBI Report: Hawaii In the Aloha state, you are very unlikely to be murdered! However, it is considerably more likely that your house might be burgled, your car might be stolen, or you might be punched in the nose. That said, Hawaii is still a great place to vacation or, if you are very lucky, to live. The following numbers represent the crime rates per 100,000 population in Hawaii: murder, 2.6; rape, $33.4$; robbery, $93.3$; house burglary, $911.6$; motor vehicle theft, $550.7$; assault, $125.3$ (Source: Crime in the United States,
U.S. Department of Justice, Federal Bureau of Investigation).
(a) Display this information in a Pareto chart, showing the crime rate for each category.
(b) Could the information as reported be displayed as a circle graph?
Explain. Hint: Other forms of crime, such as arson, are not included in the information. In addition, some crimes might occur together.

Driving: Bad Habits Driving would be more pleasant if we didn’t have to put up with the bad habits of other drivers. USA Today reported the results of a Valvolinè Oil Company survey of 500 drivèrs, in which thê drivers marked their complaints about other drivers. The top complaints turned out to be tailgating. marked by $22 \%$ of the respondents; not using turn signals, marked by $19 \%$; being cut off, marked by $16 \%$; other drivers driving too slowly, marked by $11 \%$; and other drivers being inconsiderate, marked by $8 \%$. Make a Pareto chart showing percentage of drivers listing each stated complaint. Could this information as reported be put in a circle graph? Why or why not?

Ecology: Lakes Pyramid Lake, Nevada, is described as the pride of the Paiute Indian Nation. It is a beautiful desert lake famous for very large trout. The elevation of the lake surface (feet above sea level) varies according to the annual flow of the Truckee River from Lake Tahoe. The U.S. Geological Survey provided the following data from equally spaced intervals of time over a 15 year period.

AP统计代写

统计代写|AP统计作业代写代考|Bar Graph

图 2-11 显示了两个条形图，描绘了在指定年份出生的男性和女性的预期寿命。我们来分析一下这些图的特点。

解决方案：这些图表被称为集群条形图，因为每个出生年份都有两个条形图。一根代表男性的预期寿命，另一根代表女性的预期寿命。每个条形的高度代表预期寿命（以年为单位）。

图 2-11(b) 所示的一个重要特征是比例变化。请注意，0 到 65 之间的比例被压缩。变化的尺度放大了男女寿命的明显差异，以及从 1980 年出生的人到 2010 年出生的人的预期寿命的增加。质量控制是当今生产和服务行业的一个重要方面. W. Edwards Deming 博士是全面质量管理 (TQM) 的开发者之一。在他的《走出危机》一书中，他概述了许多监控和改进服务和生产行业的策略。特别是，戴明博士建议使用一些统计方法来组织和分析来自行业的数据，以便找出问题的根源并加以纠正。

统计代写|AP统计作业代写代考|Time-Series Graph

假设您已经进行了 20 周的步行/慢跑锻炼计划，并且您每周都记录了您在 30 分钟内走过的距离。您的数据日志如表2-14所示。

(a) 制作时间序列图。
解决方案：这些数据适用于时间序列图，因为它们代表在不同时间进行的相同测量（在 30 分钟内覆盖的距离）。测量也以相等的时间间隔（每周）记录。为了制作我们的时间序列图，我们在水平刻度上按顺序列出了周。在每周上方，在垂直刻度上绘制该周所经过的距离。然后连接点。数字2−14显示时间序列图。确保标有标签。

(b) 解释从图 2-14 中，你能发现任何模式吗？
解决方案：覆盖距离似乎呈上升趋势。过去几周所覆盖的距离比前几周的距离要远大约一英里。但是，我们不能断定这种趋势会持续下去。也许您已经达到了本次培训活动的目标，现在希望保持约2.5英里在 30 分钟内。

由随时间定期进行的类似测量组成的数据集。称为时间序列。时间序列通常用于经济学、金融学、社会学、医学以及我们想要在一段时间内研究或监控类似度量的任何其他情况。时间序列图可以揭示时间序列的一些主要特征。

统计代写|AP统计作业代写代考|Lifestyle: Hide the Mess!

生活方式：隐藏混乱！一项针对 1000 名成年人的调查（《今日美国》报道）发现了一些有趣的家务秘密。当意想不到的公司来临时，我们在哪里隐藏混乱？调查显示，68%的受访者将他们的烂摊子扔进壁橱，23%把东西推到床底下，6%把东西放进浴缸里，然后3%把烂摊子放进冰箱。制作一个圆形图来显示此信息。

教育：大学教授的时间大学教授如何度过他们的时间？全国教育协会高等教育年鉴
给出了以下职业时间分配的平均分布：教学、51%; 研究，16%; 专业成长，5%; 社区服务，11%; 为学院服务，11%; 和校外咨询，6%. 制作一个饼图，显示大学教授的专业时间分配情况。

FBI 报告：夏威夷在阿罗哈州，你被谋杀的可能性很小！然而，你的房子很可能被盗，你的车可能被盗，或者你的鼻子可能被打了一拳。也就是说，夏威夷仍然是一个度假的好地方，或者，如果你很幸运，还可以居住。以下数字代表夏威夷每 100,000 人的犯罪率：谋杀，2.6；强奸，33.4; 抢劫，93.3; 入室盗窃，911.6; 机动车盗窃，550.7; 突击，125.3（资料来源：美国犯罪、
美国司法部、联邦调查局）。
(a) 在帕累托图中显示此信息，显示每个类别的犯罪率。
(b) 报告的信息可以显示为圆形图吗？
解释。提示：其他形式的犯罪，如纵火，不包括在信息中。此外，一些犯罪可能同时发生。

驾驶：坏习惯如果我们不必忍受其他司机的坏习惯，驾驶会更愉快。《今日美国》报道了 Valvolinè 石油公司对 500 名司机的调查结果，其中司机标出了他们对其他司机的抱怨。最重要的投诉结果是尾随。标记为22%受访者；不使用转向灯，标记为19%; 被切断，标记为16%; 其他司机开得太慢，标记为11%; 和其他司机不考虑周到，标记为8%. 制作一个帕累托图，显示列出每个陈述的投诉的司机百分比。报告的这些信息可以放在圆形图中吗？为什么或者为什么不？

生态：内华达州的金字塔湖被描述为派尤特印第安民族的骄傲。这是一个美丽的沙漠湖，以非常大的鳟鱼而闻名。湖面的高度（海拔英尺）根据来自太浩湖的特拉基河的年流量而变化。美国地质调查局提供了 15 年期间等间隔时间的以下数据.

Course Overview

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Organizing Data

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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|Say It with Pictures

Edward R. Tufte, in his book The Visual Display of Quantitative Information, presents a number of guidelines for producing good graphics. According to the criteria, a graphical display should

show the data:
induce the viewer to think about the substance of the graphic rather than about the methodology, the design, the technology, or other production devices;
avoid distorting what the data have to say.
As an example of a graph that violates some of the criteria, Tufte includes a graphic that appeared in a well-known newspaper. Figure 2-1(a), on the next page, shows a figure similar to the problem graphic, whereas part (b) of the figure shows a better rendition of the data display.

After completing this chapter, you will be able to answer the following questions.
(a) Look at the graph in Figure 2-1(a). Is it essentially a bar graph? Explain. What are some of the flaws of Figure $2-1$ (a) as a bar graph?
(b) Examine Figure 2-I(b), which shows the same information. Is it essentially a time-series graph? Explain. In what ways does the second graph seem to display the information in a clearer manner?
(See Problem 5 of the Chapter 2 Review Problems.)

统计代写|AP统计作业代写代考|Frequency Table

A task force to encourage car pooling did a study of one-way commuting distances of workers in the downtown Dallas area. A random sample of 60 of these workers was taken. The commuting distances of the workers in the sample are given in Table 2-1. Make a frequency table for these data.
SOLUTION:
(a) First decide how many classes you want. Five to 15 classes are usually used. If you use fewer than five classes, you risk losing too much information. If you use more than 15 classes, the data may not be sufficiently summarized. Let the

spread of the data and the purpose of the frequency table be your guides when selecting the number of classes. In the case of the commuting data, let’s use six classes.
(b) Next, find the class width for the six classes.

Note: To ensure that all the classes taken together cover the data, we need to increase the result of Step 1 to the next whole number, even if Step I produced a whole number. For instance, if the calculation in Step I produces the value 4 , we make the class width $5 .$

To find the class width for the commuting data, we observe that the largest distance commuted is 47 miles and the smallest is 1 mile. Using six classes, the clase width is 8 , since
Class width $=\frac{47-1}{6} \approx 7.7$ (increase to 8 )
(c) Now we determine the data range for each class.

The smallest commuting distance in our sample is 1 mile. We use this smallest data value as the lower class limit of the first class. Since the class width is 8 , we add 8 to 1 to find that the lower class limit for the second class is $9 .$ Following this pattern, we establish all the lower class limits. Then we fill in the upper class limits so that the classes span the entire range of data. Table $2-2$, on the next page, shows the upper and lower class limits for the commuting distance data.
(d) Now we are ready to tally the commuting distance data into the six classes and find the frequency for each class.

统计代写|AP统计作业代写代考|Histogram and Relative-Frequency Histogram

Make a histogram and a relative-frequency histogram with six bars for the data in Table 2-1 showing one-way commuting distances.

SOLUTION: The first step is to make a frequency table and a relative-frequency table with six classes. We’ll use Table $2-2$ and Table $2-3$. Figures $2-2$ and $2-3$ show the histogram and relative-frequency histogram. In both graphs, class boundaries are marked on the horizontal axis. For each class of the frequency table, make a corresponding bar with horizontal width extending from the lower boundary to the upper boundary of the respective class. For a histogram. the height of each bar is the corresponding class frequency For a relative-frequency histogram, the height of

each bar is the corresponding relative frequency. Notice that the basic shapes of the graphs are the same. The only difference involves the vertical axis. The vertical axis of the histogram shows frequencies, whereas that of the relative-frequency histogram shows relative frequencies.

Interpretation Looking at the graphs, we can observe that about half the commute distances are between 1 and 17 miles and most are less than 25 miles. It is fairly unusual for distances to exceed 40 miles or even 32 .

COMMENT The use of class boundaries in histograms assures us that the bars of the histogram touch and that no data fall on the boundaries. Both of these features are important. But a histogram displaying class boundaries may look awkward. For instance, the mileage range of $8.5$ to $16.5$ miles shown in Figure 2-2 isn’t as natural a choice as a mileage range of 8 to 16 miles. For this reason, many magazines and newspapers do not use class boundaries as labels on a histogram. Instead, some use lower class limits as labels, with the convention that a data value falling on the class limit is included in the next higher class (class to the right of the limit). Another convention is to label midpoints instead of class boundaries. Determine the default convention being used before creating frequency tables and histograms on a computer.

AP统计代写

统计代写|AP统计作业代写代考|Say It with Pictures

Edward R. Tufte 在他的《定量信息的可视化显示》一书中，提出了一些制作优质图形的指导方针。根据标准，图形显示应该

显示数据：
引导观众思考图形的实质，而不是方法、设计、技术或其他生产设备；
避免扭曲数据必须说明的内容。
作为违反某些标准的图表的示例，Tufte 包含了一张出现在知名报纸上的图表。下一页的图 2-1(a) 显示了与问题图形相似的图，而图的 (b) 部分显示了数据显示的更好再现。

完成本章后，您将能够回答以下问题。
(a) 看图 2-1(a)。它本质上是条形图吗？解释。图有哪些缺陷2−1(a) 作为条形图？
(b) 检查图 2-I(b)，它显示了相同的信息。它本质上是一个时间序列图吗？解释。第二张图似乎以何种方式更清晰地显示了信息？
（见第 2 章复习题的第 5 题。）

统计代写|AP统计作业代写代考|Frequency Table

一个鼓励拼车的工作组对达拉斯市中心地区工人的单程通勤距离进行了研究。对其中 60 名工人进行了随机抽样。样本中工人的通勤距离见表2-1。为这些数据制作频率表。
解决方案：
(a) 首先决定你想要多少个班级。通常使用 5 到 15 个类。如果您使用的类少于五个，则可能会丢失太多信息。如果您使用超过 15 个类别，则可能无法充分汇总数据。让

数据的分布和频率表的目的是您选择类数时的指南。在通勤数据的情况下，让我们使用六个类。
(b) 接下来，找出六个类的类宽。

注意：为了确保所有的类加在一起覆盖数据，我们需要将步骤 1 的结果增加到下一个整数，即使步骤 I 产生了一个整数。例如，如果步骤 I 中的计算产生值 4 ，我们将类宽度设为5.

为了找到通勤数据的类别宽度，我们观察到最大的通勤距离是 47 英里，最小的是 1 英里。使用六个班级，班级宽度为 8 ，因为
班级宽度=47−16≈7.7（增加到 8 ）
(c) 现在我们确定每个类的数据范围。

在我们的样本中，最小的通勤距离是 1 英里。我们使用这个最小的数据值作为第一类的下限。由于类宽为 8 ，我们将 8 加到 1 以发现第二类的下限为9.按照这种模式，我们建立了所有的下限。然后我们填写类上限，以便类跨越整个数据范围。桌子2−2，在下一页上，显示了通勤距离数据的上限和下限。
(d) 现在我们准备将通勤距离数据统计到六个类别中，并找到每个类别的频率。

统计代写|AP统计作业代写代考|Histogram and Relative-Frequency Histogram

为表 2-1 中的数据制作一个直方图和一个有六个条形的相对频率直方图，显示单向通勤距离。

SOLUTION: 第一步是制作一个频率表和一个六类的相对频率表。我们将使用表2−2和表2−3. 数据2−2和2−3显示直方图和相对频率直方图。在这两个图中，类边界都标记在水平轴上。对于频率表的每个类别，制作一个相应的条，其水平宽度从相应类别的下边界延伸到上边界。对于直方图。每个条的高度是相应的类频率对于相对频率直方图，高度

每个条是相应的相对频率。请注意，图形的基本形状是相同的。唯一的区别涉及垂直轴。直方图的纵轴表示频率，而相对频率直方图的纵轴表示相对频率。

解释查看图表，我们可以观察到大约一半的通勤距离在 1 到 17 英里之间，大多数不到 25 英里。距离超过 40 英里甚至 32 英里是相当不寻常的。

评论在直方图中使用类边界向我们保证直方图的条形接触并且没有数据落在边界上。这两个特性都很重要。但是显示类边界的直方图可能看起来很尴尬。例如，里程范围8.5到16.5图 2-2 中显示的里程不像 8 到 16 英里的里程范围那样自然。出于这个原因，许多杂志和报纸不使用类别边界作为直方图上的标签。相反，一些使用较低的类限制作为标签，约定落入类限制的数据值包含在下一个更高的类（限制右侧的类）中。另一个约定是标记中点而不是类边界。在计算机上创建频率表和直方图之前确定使用的默认约定。

Course Overview

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Chapter Review Problems

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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|Critical Thinking

Critical Thinking Sudoku is a puzzle consisting of squares arranged in 9 rows and 9 columns. The 81 squares are further divided into nine $3 \times 3$ square boxes. The object is to fill in the squares with numerals 1 through 9 so that each column, row, and box contains all nine numbers. However, there is a requirement that each number appear only once in any row, column, or box. Each puzzle already has numbers in some of the squares. Would it be appropriate to use a random-number table to select a digit for each blank square? Explain.
Critical Thinking Alisha wants to do a statistical study to determine how long it takes people to complete a Sudoku puzzle (see Problem 1 for a description of the puzzle). Her plan is as follows:
Download 10 different puzzles from the Internet.
Find 10 friends willing to participate.
Ask each friend to complete one of the puzzles and time him-or herself. Gather the completion times from each friend.

Describe some of the problems with Alisha’s plan for the study. (Note: Puzzles differ in difficulty, ranging from beginner to very difficult.) Are the results from Alisha’s study anecdotal, or do they apply to the general population?

Statistical Literacy You are conducting a study of students doing work-study jobs on your campus. Among the questions on the survey instrument are:
A. How many hours are you scheduled to work each week? Answer to the nearest hour.
B. How applicable is this work experience to your future employment goals?
Respond using the following scale: $1=$ not at all, 2 = somewhat, $3=$ very
(a) Suppose you take random samples from the following groups: freshmen, sophomores, juniors, and seniors. What kind of sampling technique are you using (simple random, stratified, systematic, cluster, multistage, convenience)?
(b) Describe the individuals of this study.
(c) What is the variable for question $\mathrm{A}$ ? Classify the variable as qualitative or quantitative. What is the level of the measurement?
(d) What is the variable for question B? Classify the variable as qualitative or quantitative. What is the level of the measurement?
(e) Is the proportion of responses “3 = very” to question B a statistic or a parameter?
(f) Suppose only $40 \%$ of the students you selected for the sample respond. What is the nonresponse rate? Do you think the nonresponse rate might introduce bias into the study? Explain.
(g) Would it be appropriate to generalize the results of your study to all work-study students in the nation? Explain.

统计代写|AP统计作业代写代考|Radio Talk Show: Sample Bias

Radio Talk Show: Sample Bias A radio talk show host asked listeners to respond either yes or no to the guestion, “Is the candidate who spends the most on a campaign the most likely to win?” Fifteen people called in and nine said yes. What is the implied population? What is the variable? Can you detect any bias in the selection of the sample?
Simulation: Identity Theft The U.S. Department of Justice examined all reported cases of identity theft for U.S. residents aged 16 or older. Their data show that of all the reported incidents of identity theft in a recent year, $40 \%$ involved existing credit card accounts. You are to design a simulation of seven reported identity thefts showing which ones involve existing credit card accounts and which ones do not. How would you assign the random digits 0 through 9 to the two categories “Does” and “Does not” involve existing credit card accounts? Use your random-digit assignment and the random-number table to generate the results from a random sample of seven identity thefts. If you do the simulation again, do you expect to get exactly the same results?
General: Type of Sampling Categorize the type of sampling (simple random, stratified, systematic, cluster, or convenience) used in each of the following situations.
(a) To conduct a preelection opinion poll on a proposed amendment to the state constitution, a random sample of 10 telephone prefixes (first three digits of the phone number) was selected, and all households from the phone prefixes selected were called.
(b) To conduct a study on depression among the elderly, a sample of 30 patients in one nursing home was used.
(c) To maintain quality control in a brewery, every 20 th bottle of beer coming off the production line was opened and tested.
(d) Subscribers to a new smart phone app that streams songs were assigned numbers. Then a sample of 30 subscribers was selected by using a random-number table. The subscribers in the sample were invited to rate the process for selecting the songs in the playlist.
(e) To judge the appeal of a proposed television sitcom, a random sample of 10 people from each of three different age categories was selected and those chosen were asked to rate a pilot show.
General: Gathering Data Which technique for gathering data (observational study or experiment) do you think was used in the following studies? Explain.
(a) The U.S. Census Bureau tracks population age. In 1900 , the percentage of the population that was 19 years old or younger was $44.4 \%$. In 1930 , the percentage was $38.8 \%$; in 1970 , the percentage was $37.9 \%$; and in 2000 , the percentage in that age group was down to $28.5 \%$ (Reference:
The First Measured Century, T. Caplow, L. Hicks, and B. J. Wattenberg).
(b) After receiving the same lessons, a class of 100 students was randomly divided into two groups of 50 each. One group was given a multiple-choice exam covering the material in the lessons. The other group was given an essay exam. The average test scores for the two groups were then compared.
General: Experiment How would you use a completely randomized experiment in each of the following settings? Is a placebo being used or not? Be specific and give details.
(a) A charitable nonprofit organization wants to test two methods of fundraising. From a list of 1000 past donors, half will be sent literature about the successful activities of the charity and asked to make another donation. The other 500 donors will be contacted by phone and asked to make another donation. The percentage of people from each group who make a new donation will be compared.

统计代写|AP统计作业代写代考|Student Life: Data Collection Project

I Student Life: Data Collection Project Make a statistical profile of your own statistics class. Items of interest might be
(a) Height, age, gender, pulse, number of siblings, marital status
(b) Number of college credit hours completed (as of beginning of term); grade point average
(c) Major; number of credit hours enrolled in this term
(d) Number of scheduled work hours per week
(e) Distance from residence to first class; time it takes to travel from residence to first class
(f) Year, make, and color of car usually driven
What directions would you give to people answering these questions? For instance, how accurate should the measurements be? Should age be recorded as of last birthday?
Census: Web Site Census and You, a publication of the Census Bureau, indicates that “Wherever your Web journey ends up, it should start at the Census Bureau’s site.” Find the Census Bureau’s web site as well as the site for FedStats, another extensive site offering links to federal data. The Census Bureau site touts itself as the source of “official statistics.” But it is willing to share the spotlight. The web site now has links to other “official” sources: other federal agencies, foreign statistical agencies, and state data centers. If you have access to the Internet, try the Census Bureau’s site.
Focus Problem: Fireflies Suppose you are conducting a study to compare firefly populations exposed to normal daylight/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 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.
(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?

AP统计代写

统计代写|AP统计作业代写代考|Critical Thinking

批判性思维数独是一个由 9 行 9 列排列的正方形组成的谜题。81个方格又分为九个3×3方盒。目标是用数字 1 到 9 填充方格，以便每列、每行和每框都包含所有 9 个数字。但是，要求每个数字在任何行、列或框中只出现一次。每个拼图的某些方块中已经有数字。使用随机数表为每个空白方块选择一个数字是否合适？解释。
批判性思维 Alisha 想要进行一项统计研究，以确定人们完成数独谜题需要多长时间（有关谜题的描述，请参见问题 1）。她的计划如下：
从网上下载 10 个不同的谜题。
找到10个愿意参与的朋友。
请每位朋友完成其中一个谜题并为他或她自己计时。收集每个朋友的完成时间。

描述 Alisha 的研究计划中的一些问题。（注意：谜题的难度不同，从初学者到非常困难。）Alisha 的研究结果是轶事，还是适用于一般人群？

统计素养你正在对在你的校园里勤工俭学的学生进行研究。调查工具的问题包括：
A. 您每周计划工作多少小时？回答最近的时间。
B. 这种工作经验对您未来的就业目标的适用程度如何？
使用以下量表回答：1=一点也不，2 = 有点，3=非常
(a) 假设您从以下组中随机抽取样本：大一、大二、大三和大四。您使用哪种抽样技术（简单随机、分层、系统、集群、多阶段、方便）？
(b) 描述本研究的个体。
(c) 问题的变量是什么一种? 将变量分类为定性或定量。测量水平是多少？
(d) 问题 B 的变量是什么？将变量分类为定性或定量。测量水平是多少？
(e) 对问题 B 的回答“3 = 非常”的比例是统计量还是参数？
(f) 仅假设40%您为样本选择的学生做出了回应。什么是不回复率？您认为不答复率可能会给研究带来偏见吗？解释。
(g) 将你的研究结果推广到全国所有勤工俭学的学生是否合适？解释。

统计代写|AP统计作业代写代考|Radio Talk Show: Sample Bias

电台脱口秀：偏见样本电台脱口秀主持人要求听众回答“是”或“否”的猜测，“在竞选中花费最多的候选人最有可能获胜吗？” 十五个人打来电话，九个人答应了。什么是隐含人口？变量是什么？您能在样本选择中发现任何偏差吗？
模拟：身份盗窃美国司法部审查了所有报告的 16 岁或以上美国居民身份盗窃案件。他们的数据显示，在最近一年报告的所有身份盗窃事件中，40%涉及现有的信用卡账户。你要设计一个模拟报告的七起身份盗窃事件，显示哪些涉及现有信用卡账户，哪些不涉及。您如何将随机数字 0 到 9 分配给涉及现有信用卡帐户的“是否”和“不”这两个类别？使用您的随机数字分配和随机数字表从七个身份盗窃的随机样本中生成结果。如果您再次进行模拟，您是否期望得到完全相同的结果？
一般：抽样类型对以下每种情况下使用的抽样类型（简单随机、分层、系统、整群或方便）进行分类。
(a) 为了对州宪法的拟议修正案进行选举前民意调查，随机抽取了 10 个电话前缀（电话号码的前三位），并呼叫了所选电话前缀中的所有家庭。
(b) 为了对老年人的抑郁症进行研究，使用了一个疗养院的 30 名患者的样本。
(c) 为了保持啤酒厂的质量控制，每 20 瓶从生产线上下来的啤酒都要打开并进行测试。
(d) 为流媒体歌曲的新智能手机应用程序的订阅者分配了号码。然后使用随机数表选择 30 个订阅者作为样本。样本中的订阅者被邀请对在播放列表中选择歌曲的过程进行评分。
(e) 为判断拟定电视情景喜剧的吸引力，从三个不同年龄组的每一个中随机抽取 10 人样本，并要求被选中的人对试播节目进行评分。
概述：收集数据您认为以下研究中使用了哪种数据收集技术（观察性研究或实验）？解释。
(a) 美国人口普查局追踪人口年龄。1900年，19岁或以下人口的百分比是44.4%. 1930年，这个百分比是38.8%; 1970年，这个百分比是37.9%; 而在 2000 年，该年龄组的百分比下降到28.5%（参考：
第一个测量世纪，T. Caplow、L. Hicks 和 BJ Wattenberg）。
(b) 上完相同的课后，一个班 100 名学生被随机分成两组，每组 50 人。一组接受了涵盖课程材料的多项选择题考试。另一组进行了论文考试。然后比较两组的平均考试成绩。
常规：实验您将如何在以下每种设置中使用完全随机的实验？是否使用安慰剂？要具体并提供详细信息。
(a) 一个慈善非营利组织想要测试两种筹款方法。在过去的 1000 名捐赠者名单中，有一半将被发送有关该慈善活动成功活动的文献，并被要求再次捐赠。将通过电话联系其他 500 名捐赠者，并要求他们再次捐赠。将比较每组进行新捐赠的人的百分比。

统计代写|AP统计作业代写代考|Student Life: Data Collection Project

I 学生生活：数据收集项目为您自己的统计课程制作统计资料。感兴趣的项目可能是
(a) 身高、年龄、性别、脉搏、兄弟姐妹数量、婚姻状况
(b) 完成的大学学分数量（截至学期开始时）；平均绩点
(c) 专业；本学期注册的学时
数 (d) 每周预定工作时数
(e) 从住所到头等舱的距离；从住所到头等舱所需的时间
(f) 通常驾驶的汽车的年份、品牌和颜色
您会给回答这些问题的人哪些指示？例如，测量应该有多准确？年龄应该记录到上一个生日吗？
Census: Web Site Census and You 是人口普查局的一份出版物，它指出“无论您的网络旅程结束于何处，都应该从人口普查局的站点开始。” 查找人口普查局的网站以及 FedStats 的网站，这是另一个提供联邦数据链接的广泛网站。人口普查局网站自诩为“官方统计数据”的来源。但它愿意分享聚光灯。该网站现在有其他“官方”来源的链接：其他联邦机构、外国统计机构和州数据中心。如果您可以访问互联网，请尝试人口普查局的网站。
焦点问题：萤火虫假设您正在进行一项研究，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，只是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。
(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？

Course Overview

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Introduction to Experimental Design

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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

Chapter 2 Completely Randomized Designs | ANOVA and Mixed Models — 统计代写|AP统计作业代写代考|Introduction to Experimental Design

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.

统计代写|AP统计作业代写代考|Introduction to 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

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Random Samples

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Levels of Measurement

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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|Identify the type of data

(a) Taos, Acoma, Zuni, and Cochiti are the names of four Native American pueblos from the population of names of all Native American pueblos in Arizona and New Mexico.
SOLUTION: These data are at the nominal level. Notice that these data values are simply names. By looking at the name alone, we cannot determine if one name is “greater than or less than” another. Any ordering of the names would be numerically meaningless.
(b) In a high school graduating class of 319 students, Jim ranked 25 th, June ranked 19 th, Walter ranked 10th, and Julia ranked 4th, where 1 is the highest rank. SOLUTION: These data are at the ordinal level. Ordering the data clearly makes sense. Walter ranked higher than June. Jim had the lowest rank, and Julia the highest. However, numerical differences in ranks do not have meaning. The difference between June’s and Jim’s ranks is 6 , and this is the same difference that exists between Walter’s and Julia’s ranks. However, this difference doesn’t really mean anything significant. For instance, if you looked at grade point average, Walter and Julia may have had a large gap between their grade point averages, whereas June and Jim may have had closer grade point averages. In any ranking system, it is only the relative standing that matters. Computed differences between ranks are meaningless.
(c) Body temperatures (in degrees Celsius) of trout in the Yellowstone River. SOLUTION: These data are at the interval level. We can certainly order the data, and we can compute meaningful differences. However, for Celsius-scale temperatures, there is not an inherent starting point. The value $0^{\circ} \mathrm{C}$ may seem to be a starting point, but this value does not indicate the state of “no heat.” Furthermore, it is not correct to say that $20^{\circ} \mathrm{C}$ is twice as hot as $10^{\circ} \mathrm{C}$.

(d) Length of trout swimming in the Yellowstone River. SOLUTION: These data are at the ratio level. An 18-inch trout is three times as long as a 6-inch trout. Observe that we can divide 6 into 18 to determine a meaningful ratio of trout lengths.

In summary, there are four levels of measurement. The nominal level is considered the lowest, and in ascending order we have the ordinal, interval, and ratio levels. In general, ealeulations based on a partieular level of measurement may not be appropriate for a lower level.

统计代写|AP统计作业代写代考|What Does the Level of Measurement Tell Us?

The level of measurement tells us which arithmetic processes are appropriate for the data. This is important because different statistical processes require various kinds of arithmetic. In some instances all we need to do is count the number of data that meet specified criteria. In such cases nominal (and higher) data levels are all appropriate. In other cases we need to order the data, so nominal data would not be suitable. Many other statistical processes require division, so data need to be at the ratio level. Just keep the nature of the data in mind before beginning statistical computations.

统计代写|AP统计作业代写代考|Critical Thinking

“Data! Data! Data! ” he cried impatiently. “I can’t make bricks without clay.” Sherlock Holmes said these words in The Adventure of the Copper Beeches by Sir Arthur Conan Doyle.

Reliable statistical conclusions require reliable data. This section has provided some of the vocabulary used in discussing data. As you read a statistical study or conduct one, pay attention to the nature of the data and the ways they were collected.
When you select a variable to measure, be sure to specify the process and requirements for measurement. For example, if the variable is the weight of readyto-harvest pineapples, specify the unit of weight, the accuracy of measurement, and maybe even the particular scale to be used. If some weights are in ounces and others in grams, the data are fairly useless.

Another concern is whether or not your measurement instrument truly measures the variable. Just asking people if they know the geographic location of the island nation of Fiji may not provide accurate results. The answers may reflect the fact that the respondents want you to think they are knowledgeable. Asking people to locate Fiji on a map may give more reliable results.
The level of measurement is also an issue. You can put numbers into a calculator or computer and do all kinds of arithmetic. However, you need to judge whether the operations are meaningful. For ordinal data such as restaurant rankings, you can’t conclude that a 4-star restaurant is “twice as good” as a 2-star restaurant, even though the number 4 is twice 2 .Are the data from a sample, or do they comprise the entire population? Sample data can vary from one sample to another! This means that if you are studying the same statistic from two different samples of the same size, the data values may be different. In fact, the ways in which sample statistics vary among different samples of the same size will be the focus of our study from Section $6.4$ on.

AP统计代写

统计代写|AP统计作业代写代考|Identify the type of data

(a) Taos、Acoma、Zuni 和 Cochiti 是亚利桑那州和新墨西哥州所有美洲原住民普韦布洛人的名字中的四个美洲原住民普韦布洛人的名字。
解决方案：这些数据处于标称水平。请注意，这些数据值只是名称。仅通过查看名称，我们无法确定一个名称是否“大于或小于”另一个名称。名称的任何排序在数字上都是没有意义的。
(b) 在一个 319 名学生的高中毕业班中，Jim 排名第 25，June 排名第 19，Walter 排名第 10，Julia 排名第 4，其中 1 是最高排名。解决方案：这些数据处于序数级别。对数据进行排序显然是有意义的。沃尔特的排名高于六月。吉姆排名最低，朱莉娅最高。但是，等级的数值差异没有意义。June 和 Jim 的等级之间的差异是 6 ，这与 Walter 和 Julia 的等级之间存在的差异相同。但是，这种差异并不意味着任何重大意义。例如，如果您查看平均成绩，Walter 和 Julia 的平均成绩之间可能存在很大差距，而 June 和 Jim 的平均成绩可能更接近。在任何排名系统中，只有相对地位才是重要的。
(c) 黄石河中鳟鱼的体温（摄氏度）。解决方案：这些数据处于区间级别。我们当然可以对数据进行排序，并且可以计算出有意义的差异。然而，对于摄氏温度，没有固有的起点。价值0∘C似乎是一个起点，但这个值并不表示“没有热量”的状态。此外，这样说是不正确的20∘C是热的两倍10∘C.

(d) 鳟鱼在黄石河中游泳的长度。解决方案：这些数据处于比率级别。一条 18 英寸的鳟鱼是 6 英寸的鳟鱼的三倍。观察到我们可以将 6 分为 18 来确定一个有意义的鳟鱼长度比率。

总之，有四个级别的测量。名义水平被认为是最低的，按升序我们有序数、间隔和比率水平。一般而言，基于特定测量水平的评估可能不适用于较低水平。

统计代写|AP统计作业代写代考|What Does the Level of Measurement Tell Us?

测量级别告诉我们哪些算术过程适合数据。这很重要，因为不同的统计过程需要各种算术。在某些情况下，我们需要做的就是计算符合指定标准的数据数量。在这种情况下，标称（和更高）数据级别都是合适的。在其他情况下，我们需要对数据进行排序，因此名义数据不合适。许多其他统计过程需要除法，因此数据需要处于比率级别。在开始统计计算之前，请记住数据的性质。

统计代写|AP统计作业代写代考|Critical Thinking

“数据！数据！数据！”他不耐烦地叫道。“没有粘土，我不能做砖。” 夏洛克·福尔摩斯在亚瑟·柯南·道尔爵士的《铜山毛榉历险记》中说过这些话。

可靠的统计结论需要可靠的数据。本节提供了一些用于讨论数据的词汇。在阅读或进行统计研究时，请注意数据的性质和收集方式。
当您选择要测量的变量时，请务必指定测量的过程和要求。例如，如果变量是准备收获的菠萝的重量，请指定重量单位、测量精度，甚至可能要使用特定的秤。如果某些重量以盎司为单位，而另一些以克为单位，则数据是毫无用处的。

另一个问题是您的测量仪器是否真正测量了变量。仅仅询问人们是否知道斐济岛国的地理位置可能无法提供准确的结果。答案可能反映了受访者希望您认为他们知识渊博的事实。让人们在地图上定位斐济可能会得到更可靠的结果。
测量水平也是一个问题。您可以将数字输入计算器或计算机，然后进行各种算术运算。但是，您需要判断这些操作是否有意义。对于餐厅排名等序数数据，即使数字 4 是 2 的两倍，也不能得出 4 星级餐厅比 2 星级餐厅“好两倍”的结论。是来自样本的数据，还是做他们包括整个人口？样本数据可能因样本而异！这意味着，如果您从两个相同大小的不同样本中研究相同的统计数据，则数据值可能不同。事实上，相同大小的不同样本之间样本统计量的变化方式将是我们研究的重点。6.4在。

Course Overview

Course Content

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

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

贝叶斯方法代考

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。

机器学习代写

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

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

统计代写|AP统计作业代写代考|Getting Started

Posted on 2022年4月13日2022年4月13日 by statistics-lab

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。

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？

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

Course Content

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

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

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写