BUS 241 - 统计代写答疑辅导

标签： BUS 241

统计代写|商业分析作业代写Statistical Modelling for Business代考|Excel, MegaStat, and Minitab for Statistics

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

statistics-lab™ 为您的留学生涯保驾护航在代写商业分析Statistical Modelling for Business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。我们的专家在代写商业分析Statistical Modelling for Business方面经验极为丰富，各种代写商业分析Statistical Modelling for Business相关的作业也就用不着说。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|Excel, MegaStat, and Minitab for Statistics

In this book we use three types of software to carry out statistical analysis – Excel 2013, MegaStat, and Minitab $17 .$ Excel is, of course, a general purpose electronic spreadsheet program and analytical tool. The analysis ToolPak in Excel includes many procedures for performing various kinds of basic statistical analyses. MegaStat is an add-in package that is specifically designed for performing statistical analysis in the Excel spreadsheet environment. Minitab is a computer package designed expressly for conducting statistical analysis. It is widely used at many colleges and universities and in a large number of business organizations. The principal advantage of Excel is that, because of its broad acceptance among students and professionals as a multipurpose analytical tool, it is both well-known and widely available. The advantages of a special-purpose statistical software package like Minitab are that it provides a far wider range of statistical procedures and it offers the experienced analyst a range of options to better control the analysis. The advantages of MegaStat include (1) its ability to perform a number of statistical calculations that are not automatically done by the procedures in the Excel ToolPak and (2) features that make it easier to use than Excel for a wide variety of statistical analyses. In addition, the output obtained by using MegaStat is automatically placed in a standard Excel spreadsheet and can be edited by using any of the features in Excel. MegaStat can be copied from the book’s website. Excel, MegaStat, and Minitab, through built-in functions, programming languages, and macros, offer almost limitless power. Here, we will limit our attention to procedures that are easily accessible via menus without resorting to any special programming or advanced features.

Commonly used features of Excel 2013, MegaStat, and Minitab 17 are presented in this chapter along with an initial application-the construction of a time series plot of the gas mileages in Table 1.7. You will find that thẻ limited instructions included hêré, alông with thê built-in hêlp féatures ớ all thieê sơftware packages, will serve as a starting point from which you can discover a variety of other procedures and options. Much more detailed descriptions of Minitab 17 can be found in other sources, in particular in the manual Getting Started with Minitab $17 .$ This manual is available as a pdf file, viewable using Adobe Acrobat Reader, on the Minitab Inc. website-go to http://www.minitab.com/en-us/support/documentation/ to download the manual. This manual is also available online at http://support.minitab.com/en-us/minitab/17/getting-started/. Similarly, there are a number of alternative reference materials for Microsoft Excel 2013. Of course, an understanding of the related statistical concepts is essential to the effective use of any statistical software package.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Getting Started with Excel

Because Excel 2013 may be new to some readers, and because the Excel 2013 window looks somewhat different from previous versions of Excel, we will begin by describing some characteristics of the Excel 2013 window. Versions of Excel prior to 2007 employed many drop-down menus. This meant that many features were “hidden” from the user, which resulted in a steep learning curve for beginners. Beginning with Excel 2010, Microsoft tried to reduce the number of features that are hidden in drop-down menus. Therefore, Excel 2013 displays all of the applicable commands needed for a particular type of task at the top of the Excel window. These commands are represented by a tab-and-group arrangement called the ribbon-see the right side of the illustration of an Excel 2013 window on the next page. The commands displayed in the ribbon are regulated by a series of tabs located near the top of the ribbon. For example, in the illustration, the Home tab is selected. If we selected a different tab, say, for example, the Page Layout tab, the commands displayed by the ribbon would be different.
We now briefly describe some basic features of the Excel 2013 window:
1 File button: By clicking on this button, the user obtains a menu of often used commands-for example, Open, Save, Print, and so forth. This menu also provides access to a large number of Excel options settings.
$2 \mathrm{~ T a ̉ b s : ~ C l i c k i n g ̄ ~ o ̄ n ~ a ~ t a b b ~ r e ̂ s u l t s ~ i n ~ a ~ r i b b o ́ n ~ đ i s p l a y ~ o ̛ f ~ f e ̉ a t u}$ type of task. For example, when the Home tab is selected (as in the figure), the features, commands, and options displayed by the ribbon are all related to making entries into the Excel worksheet. As another example, if the Formulas tab is selected, all of the features, commands, and options displayed in the ribbon relate to using formulas in the Excel worksheet.
Appendix $1.1$
Getting Started with Excel
3 Quick access toolbar: This toolbar displays buttons that provide shortcuts to often-used commands. Initially, this toolbar displays Save, Undo, and Redo buttons. The user can customize this toolbar by adding shortcut buttons for other commands (such as New, Open, Quick Print, and so forth). This can be done by clicking on the arrow button directly to the right of the Quick Access toolbar and by making selections from the “Customize” drop-down menu that appears.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Select Home : Format : Row Height

This notation indicates that we first select the Home tab on the ribbon, then we select Format from the Cells Group on the ribbon, and finally we select Row Height from the Format drop-down menu.

For many of the statistical and graphical procedures in Excel, it is necessary to provide a range of cells to specify the location of data in the spreadsheet. Generally, the range may be specified either by typing the cell locations directly into a dialog box or by dragging the selected range with the mouse. Although for the experienced user, it is usually easier to use the mouse to select a range, the instructions that follow will, for precision and clarity, specify ranges by typing in cell locations. The selected range may include column or variable labels-labels at the tops columns that serve to identify variables. When the selected range includes such labels, it is important to select the “Labels check box” in the analysis dialog box.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Excel, MegaStat, and Minitab for Statistics

在本书中，我们使用三种类型的软件进行统计分析——Excel 2013、MegaStat 和 Minitab17.Excel 当然是一种通用的电子表格程序和分析工具。Excel 中的分析工具包包括许多用于执行各种基本统计分析的程序。MegaStat 是一个插件包，专为在 Excel 电子表格环境中执行统计分析而设计。Minitab 是专为进行统计分析而设计的计算机软件包。它在许多高校和大量的商业组织中被广泛使用。Excel 的主要优势在于，由于它作为一种多功能分析工具在学生和专业人士中被广泛接受，因此它广为人知且应用广泛。像 Minitab 这样的专用统计软件包的优势在于它提供了范围更广的统计程序，并为经验丰富的分析师提供了一系列选项来更好地控制分析。MegaStat 的优点包括 (1) 能够执行 Excel ToolPak 中的程序无法自动完成的大量统计计算，以及 (2) 比 Excel 更易于使用的各种统计分析功能。此外，使用 MegaStat 获得的输出会自动放置在标准 Excel 电子表格中，并且可以使用 Excel 中的任何功能进行编辑。MegaStat 可以从本书的网站上复制。Excel、MegaStat 和 Minitab 通过内置函数、编程语言和宏，提供几乎无限的功能。这里，

本章介绍 Excel 2013、MegaStat 和 Minitab 17 的常用功能以及初始应用程序 – 表 1.7 中的油耗时间序列图的构建。您会发现包括 hêré 在内的有限说明，以及内置帮助功能 ớ 所有 thieê 软件包，将作为您可以发现各种其他程序和选项的起点。可以在其他来源中找到有关 Minitab 17 的更详细的描述，特别是在 Minitab 入门手册中17.本手册以 PDF 文件的形式提供，可使用 Adobe Acrobat Reader 在 Minitab Inc. 网站上查看 – 请访问 http://www.minitab.com/en-us/support/documentation/ 下载手册。本手册也可从 http://support.minitab.com/en-us/minitab/17/getting-started/ 在线获取。同样，Microsoft Excel 2013 也有许多替代参考资料。当然，了解相关统计概念对于有效使用任何统计软件包都是必不可少的。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Getting Started with Excel

由于 Excel 2013 对某些读者来说可能是新的，并且由于 Excel 2013 窗口看起来与以前版本的 Excel 有所不同，我们将首先描述 Excel 2013 窗口的一些特征。2007 年之前的 Excel 版本使用了许多下拉菜单。这意味着许多功能对用户“隐藏”，导致初学者学习曲线陡峭。从 Excel 2010 开始，Microsoft 尝试减少隐藏在下拉菜单中的功能数量。因此，Excel 2013 在 Excel 窗口顶部显示特定类型任务所需的所有适用命令。这些命令由称为功能区的选项卡和组排列表示 – 请参见下一页 Excel 2013 窗口插图的右侧。功能区中显示的命令由功能区顶部附近的一系列选项卡控制。例如，在插图中，选择了 Home 选项卡。如果我们选择了不同的选项卡，例如“页面布局”选项卡，则功能区显示的命令会有所不同。
我们现在简要介绍一下 Excel 2013 窗口的一些基本功能：
1 文件按钮：通过单击此按钮，用户可以获得一个常用命令菜单，例如打开、保存、打印等。此菜单还提供对大量 Excel 选项设置的访问。
̉đ̛̉2 吨一种̉bs: Cl一世Cķ一世nḠ 这̄n 一种吨一种bb r和̂s在l吨s 一世n 一种 r一世bb这́n D一世spl一种是这̛F F和̉一种吨在任务类型。例如，当 Home 选项卡被选中时（如图所示），功能区显示的功能、命令和选项都与在 Excel 工作表中输入条目有关。作为另一个示例，如果选择了“公式”选项卡，则功能区中显示的所有功能、命令和选项都与在 Excel 工作表中使用公式有关。
附录1.1
Excel
3 入门快速访问工具栏：此工具栏显示提供常用命令快捷方式的按钮。最初，此工具栏显示保存、撤消和重做按钮。用户可以通过为其他命令（例如新建、打开、快速打印等）添加快捷按钮来自定义此工具栏。这可以通过直接单击快速访问工具栏右侧的箭头按钮并从出现的“自定义”下拉菜单中进行选择来完成。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Select Home : Format : Row Height

该符号表示我们首先选择功能区上的主页选项卡，然后从功能区上的单元格组中选择格式，最后从格式下拉菜单中选择行高。

对于 Excel 中的许多统计和图形程序，需要提供一系列单元格来指定数据在电子表格中的位置。通常，可以通过直接在对话框中键入单元格位置或通过用鼠标拖动所选范围来指定范围。尽管对于有经验的用户来说，使用鼠标选择范围通常更容易，但为了精确和清晰，下面的说明将通过键入单元格位置来指定范围。选定的范围可以包括列或变量标签——位于顶部列的用于识别变量的标签。当所选范围包括此类标签时，请务必在分析对话框中选中“标签复选框”。

统计代写|商业分析作业代写Statistical Modelling for Business代考请认准statistics-lab™

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|商业分析作业代写Statistical Modelling for Business代考|More about Surveys and Errors in Survey

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|More about Surveys and Errors in Survey

统计代写|商业分析作业代写Statistical Modelling for Business代考|Types of survey questions

Survey instruments can use dichotomous (“yes or no”), multiple-choice, or open-ended questions. Each type of question has its benefits and drawbacks. Dichotomous questions are usually clearly stated, can be answered quickly, and yield data that are easily analyzed. However, the information gathered may be limited by this two-option format. If we limit voters to expressing support or disapproval for stem-cell research, we may not learn the nuanced reasoning that voters use in weighing the merits and moral issues involved. Similarly, in today’s heterogeneous world, it would be unusual to use a dichotomous question to categorize a person’s religious preferences. Asking whether respondents are Christian or non-Christian (or to use any other two categories like Jewish or non-Jewish; Muslim or nonMuslim) is certain to make some people feel their religion is being slighted. In addition, this is a crude and unenlightening way to learn about religious preferences.

Multiple-choice questions can assume several different forms. Sometimes respondents are asked to choose a response from a list (for example, possible answers to the religion question could be Jewish; Christian; Muslim; Hindu; Agnostic; or Other). Other times, respondents are asked to choose an answer from a numerical range. We could ask the question:
“In your opinion, how important are SAT scores to a college student’s success?”
Not important at all $1 \quad 2 \quad 3 \quad 4 \quad 5$ Extremely important
These numerical responses are usually summarized and reported in terms of the average response, whose size tells us something about the perceived importance. The Zagat restaurant survey (www.zagat.com) asks diners to rate restaurants’ food, décor, and service, each on a scale of 1 to 30 points, with a 30 representing an incredible level of satisfaction. Although the Zagat scale has an unusually wide range of possible ratings, the concept is the same as in the more common 5-point scale.

Open-ended questions typically provide the most honest and complete information because there are no suggested answers to divert or bias a person’s response. This kind of question is often found on instructor evaluation forms distributed at the end of a college course. College students at Georgetown University are asked the open-ended question, “What comments would you give to the instructor?’ The responses provide the instructor feedback that may be missing from the initial part of the teaching evaluation survey, which consists of numerical multiple-choice ratings of various aspects of the course. While these numerical ratings can be used to compare instructors and courses, there are no easy comparisons of the diverse responses instructors receive to the open-ended question. In fact, these responses are often seen only by the instructor and are useful, constructive tools for the teacher despite the fact they cannot be readily summarized.

Survey questionnaires must be carefully constructed so they do not inadvertently bias the results. Because survey design is such a difficult and sensitive process, it is not uncommon for a pilot survey to be taken before a lot of time, effort, and financing go into collecting a large amount of data. Pilot surveys are similar to the beta version of a new electronic product; they are tested out with a smaller group of people to work out the “kinks” before being used on a larger scale. Determination of the sample size for the final survey is an important process for many reasons. If the sample size is too large, resources may be wasted during the data collection. On the other hand, not collecting enough data for a meaningful analysis will obviously be detrimental to the study. Fortunately, there are several formulas that will help decide how large a sample should be, depending on the goal of the study and various other factors.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Types of surveys

There are several different survey types, and we will explore just a few of them. The phone survey is particularly well-known (and often despised). A phone survey is inexpensive and usually conducted by callers who have very little training. Because of this and the impersonal nature of the medium, the respondent may misunderstand some of the questions. A further drawback is that some people cannot be reached and that others may refuse to answer some or all of the questions. Phone surveys are thus particularly prone to have a low response rate.
The response rate is the proportion of all people whom we attempt to contact that actually respond to a survey. A low response rate can destroy the validity of a survey’s results.
It can be difficult to collect good data from unsolicited phone calls because many of us resent the interruption. The calls often come at inopportune times, intruding on a meal or arriving just when we have climbed a ladder with a full can of paint. No wonder we may fantasize about turning the tables on the callers and calling them when it is least convenient.

Numerous complaints have been filed with the Federal Trade Commission (FTC) about the glut of marketing and survey telephone calls to private residences. The National Do Not Call Registry was created as the culmination of a comprehensive, three-year review of the Telemarketing Sales Rule (TSR) (www.ftc.gov/donotcall/). This legislation allows people to enroll their phone numbers on a website so as to prevent most marketers from calling them.
Self-administered surveys, or mail surveys, are also very inexpensive to conduct. However, these also have their drawbacks. Often, recipients will choose not to reply unless they receive some kind of financial incentive or other reward. Generally, after an initial mailing, the response rate will fall between 20 and 30 percent. Response rates can be raised with successive follow-up reminders, and after three contacts, they might reach between 65 and 75 percent. Unfortunately, the entire process can take significantly longer than a phone survey would.

Web-based surveys have become increasingly popular, but they suffer from the same problems as mail surveys. In addition, as with phone surveys, respondents may record their true reactions incorrectly because they have misunderstood some of the questions posed.
A personal interview provides more control over the survey process. People selected for interviews are more likely to respond because the questions are being asked by someone face-to-face. Questions are less likely to be misunderstood because the people conducting the interviews are typically trained employees who can clear up any confusion arising during the process. On the other hand, interviewers can potentially “lead” a respondent by body language which signals approval or disapproval of certain sorts of answers. They can also prompt certain replies by providing too much information. Mall surveys are examples of personal interviews. Interviewers approach shoppers as they pass by and ask them to answer the survey questions. Response rates around 50 percent are typical. Personal interviews are more costly than mail or phone surveys. Obviously, the objective of the study will be important in deciding upon the survey type employed.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Errors of observation

As discussed in Section 1.4, the opinions of those who bother to complete a voluntary response survey may be dramatically different from those who do not. (Recall the Ann Landers question about having children.) The viewer voting on the popular television show American Idol is another illustration of selection bias, because only those who are interested in the outcome of the show will bother to phone in or text message their votes. The results of the voting are not representative of the performance ratings the country would give as a whole.
Errors of observation occur when data values are recorded incorrectly. Such errors can be caused by the data collector (the interviewer), the survey instrument, the respondent, or the data collection process. For instance, the manner in which a question is asked can influence the response. Or, the order in which questions appear on a questionnaire can influence the survey results. Or, the data collection method (telephone interview, questionnaire, personal interview, or direct observation) can influence the results. A recording error occurs when either the respondent or interviewer incorrectly marks an answer. Once data are collected from a survey, the results are often entered into a computer for statistical analysis. When transferring data from a survey form to a spreadsheet program like Excel, Minitab, or MegaStat, there is potential for entering them incorrectly. Before the survey is administered, the questions need to be very carefully worded so that there is little chance of misinterpretation. A poorly framed question might yield results that lead to unwarranted decisions. Scaled questions are particularly susceptible to this type of error. Consider the question “How would you rate this course?” Without a proper explanation, the respondent may not know whether “1” or ” 5 ” is the best.

If the survey instrument contains highly sensitive questions and respondents feel compelled to answer, they may not tell the truth. This is especially true in personal interviews. We then have what is called response bias. A surprising number of people are reluctant to be candid about what they like to read or watch on television. People tend to overreport “good” activities like reading respected newspapers and underreport their “bad” activities like delighting in the National Fnquirer’s stories of alien ahductions and celehrity meltdewns. Iniggine, then, the difficully in getting henest inswers abeut pevple’s ganbling hab its, drug use, or sexual histories. Response bias can also occur when respondents are asked slanted questions whose wording influences the answer received. For example, consider the following question:
Which of the following best describes your views on gun control?
1 The government should take away our guns, leaving us defenseless against heavily armed criminals.
2 We have the right to keep and bear arms.
This question is biased toward eliciting a response against gun control.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Types of survey questions

调查工具可以使用二分法（“是或否”）、多项选择或开放式问题。每种类型的问题都有其优点和缺点。二分法问题通常表述清楚，可以快速回答，并产生易于分析的数据。但是，收集的信息可能会受到这种二选一格式的限制。如果我们限制选民表达对干细胞研究的支持或反对，我们可能无法了解选民在权衡所涉及的优点和道德问题时使用的微妙推理。同样，在当今异质的世界中，使用二分法问题对一个人的宗教偏好进行分类是不寻常的。询问受访者是基督徒还是非基督徒（或使用任何其他两个类别，如犹太人或非犹太人；穆斯林或非穆斯林）肯定会让一些人觉得他们的宗教受到轻视。此外，这是了解宗教偏好的一种粗鲁且无启发性的方式。

多项选择题可以采用几种不同的形式。有时会要求受访者从列表中选择一个答案（例如，宗教问题的可能答案可能是犹太人；基督教；穆斯林；印度教；不可知论者；或其他）。其他时候，受访者被要求从一个数字范围中选择一个答案。我们可以问这样一个问题：
“在您看来，SAT 成绩对大学生的成功有多重要？”
一点都不重要12345极其重要
这些数值响应通常根据平均响应进行总结和报告，其大小告诉我们一些关于感知重要性的信息。Zagat 餐厅调查 (www.zagat.com) 要求食客对餐厅的食物、装饰和服务进行评分，每项评分从 1 到 30 分，其中 30 分代表令人难以置信的满意度。尽管 Zagat 量表具有异常广泛的可能评级范围，但其概念与更常见的 5 点量表相同。

开放式问题通常提供最诚实和最完整的信息，因为没有建议的答案来转移或偏向一个人的反应。这种问题经常出现在大学课程结束时分发的教师评估表上。乔治城大学的大学生被问到一个开放式问题，“你会给导师什么意见？” 回答提供了教师反馈，教学评估调查的初始部分可能缺少该反馈，该调查由课程各个方面的数字多项选择评分组成。虽然这些数字评分可用于比较教师和课程，但很难比较教师对开放式问题的不同回答。实际上，

调查问卷必须仔细构建，以免无意中使结果产生偏差。由于调查设计是一个如此困难和敏感的过程，在大量时间、精力和资金用于收集大量数据之前进行试点调查的情况并不少见。试点调查类似于新电子产品的测试版；在大规模使用之前，它们会与一小群人一起进行测试，以解决“问题”。出于多种原因，确定最终调查的样本量是一个重要过程。如果样本量太大，可能会在数据收集过程中浪费资源。另一方面，没有为有意义的分析收集足够的数据显然会损害研究。幸运的是，

统计代写|商业分析作业代写Statistical Modelling for Business代考|Types of surveys

有几种不同的调查类型，我们将只探讨其中的几个。电话调查特别有名（并且经常被鄙视）。电话调查费用不高，通常由受过很少培训的呼叫者进行。由于这一点以及媒体的非个人性质，受访者可能会误解一些问题。另一个缺点是无法联系到某些人，而其他人可能拒绝回答部分或全部问题。因此，电话调查特别容易得到低响应率。
回复率是我们尝试联系的所有实际回复调查的人的比例。低响应率会破坏调查结果的有效性。
可能很难从不请自来的电话中收集良好的数据，因为我们中的许多人都讨厌这种中断。这些电话通常在不合时宜的时候打来，比如打扰吃饭，或者在我们爬上装满油漆的梯子时才打来。难怪我们会幻想扭转局面并在最不方便的时候打电话给他们。

已经向联邦贸易委员会 (FTC) 提出了大量关于私人住宅的营销和调查电话过剩的投诉。全国请勿来电登记处的创建是对电话营销销售规则 (TSR) (www.ftc.gov/donotcall/) 的三年全面审查的高潮。这项立法允许人们在网站上注册他们的电话号码，以防止大多数营销人员给他们打电话。
自我管理的调查或邮寄调查的成本也很低。然而，这些也有它们的缺点。通常，收件人会选择不回复，除非他们收到某种经济激励或其他奖励。通常，在初次邮寄后，回复率将在 20% 到 30% 之间。通过连续的后续提醒可以提高响应率，并且在三个联系之后，它们可能会达到 65% 到 75% 之间。不幸的是，整个过程可能比电话调查花费的时间要长得多。

基于网络的调查变得越来越流行，但它们也面临与邮件调查相同的问题。此外，与电话调查一样，受访者可能会错误地记录他们的真实反应，因为他们误解了提出的一些问题。
个人访谈可以更好地控制调查过程。被选中参加面试的人更有可能做出回应，因为这些问题是由面对面的人提出的。问题不太可能被误解，因为进行采访的人通常是经过培训的员工，他们可以解决过程中出现的任何困惑。另一方面，采访者可能会通过肢体语言“引导”受访者，这表明对某些类型的回答表示赞同或不赞同。他们还可以通过提供太多信息来提示某些回复。商场调查是个人访谈的例子。采访者在购物者经过时接近他们并要求他们回答调查问题。50% 左右的响应率是典型的。个人访谈比邮件或电话调查更昂贵。明显地，

统计代写|商业分析作业代写Statistical Modelling for Business代考|Errors of observation

正如第 1.4 节所讨论的，那些费心完成自愿回答调查的人的意见可能与那些不参加的人有很大不同。（回想一下 Ann Landers 关于生孩子的问题。）热门电视节目《美国偶像》上的观众投票是选择偏见的另一个例证，因为只有那些对节目结果感兴趣的人才会费心打电话或发短信给他们的选票. 投票结果并不代表该国整体的绩效评级。
当数据值记录不正确时，就会发生观察错误。此类错误可能由数据收集者（访调员）、调查工具、受访者或数据收集过程引起。例如，提问的方式会影响回答。或者，问题出现在问卷上的顺序会影响调查结果。或者，数据收集方法（电话访谈、问卷调查、个人访谈或直接观察）会影响结果。当受访者或访问者错误地标记答案时，就会发生记录错误。从调查中收集数据后，通常会将结果输入计算机进行统计分析。将数据从调查表传输到 Excel、Minitab 或 MegaStat 等电子表格程序时，有可能输入错误。在进行调查之前，需要对问题进行非常仔细的措辞，以免产生误解。一个结构不佳的问题可能会产生导致无根据的决定的结果。比例问题特别容易受到此类错误的影响。考虑“你如何评价这门课程？”这个问题。如果没有适当的解释，被访者可能不知道“1”还是“5”是最好的。考虑“你如何评价这门课程？”这个问题。如果没有适当的解释，被访者可能不知道“1”还是“5”是最好的。考虑“你如何评价这门课程？”这个问题。如果没有适当的解释，被访者可能不知道“1”还是“5”是最好的。

如果调查工具包含高度敏感的问题，而受访者觉得有必要回答，他们可能不会说实话。在个人采访中尤其如此。然后我们就有了所谓的反应偏差。数量惊人的人不愿意坦诚他们喜欢在电视上阅读或观看的内容。人们倾向于高估“好”活动，例如阅读受人尊敬的报纸，而低估他们的“坏”活动，例如欣赏《国家调查报》关于外星人绑架和名人融化的故事。因此，Iniggine 很难得到关于 pevple 的嗜好、吸毒或性史的最新答案。当回答者被问及措辞会影响收到的答案的倾斜问题时，也会出现反应偏差。例如，考虑以下问题：
以下哪项最能描述您对枪支管制的看法？
1 政府应该拿走我们的枪支，让我们对全副武装的犯罪分子束手无策。
2 我们有权持有和携带武器。
这个问题偏向于引发对枪支管制的回应。

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|商业分析作业代写Statistical Modelling for Business代考|Predictive analytics, data mining

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|Predictive analytics, data mining

统计代写|商业分析作业代写Statistical Modelling for Business代考|prescriptive analytics

Predictive analytics are methods used to find anomalies, patterns, and associations in data sets, with the purpose of predicting future outcomes. Predictive analytics and data mining are terms that are sometimes used together, but data mining might more specifically be defined to be the use of predictive analytics, computer science algorithms, and information systems techniques to extract useful knowledge from huge amounts of data. It is estimated that for any data mining project, approximately 65 percent to 90 percent of the time is spent in data preparation – checking, correcting, reconciling inconsistencies in, and otherwise “cleaning” the data. Also, whereas predictive analytics methods might be most useful to decision makers when used with data mining, these methods can also be important, as we will see, when analyzing smaller data sets. Prescriptive analytics looks at internal and extemal variables and constraints, along with the predictions obtained from predictive analytics, to recommend one or more courses of action. In this book, other than intuitively using predictions from predictive analytics to suggest business improvement courses of action, we will not discuss prescriptive analytics. Therefore, returning to predictive analytics, we can roughly classify the applications of predictive analytics into six categories:
Anomaly (outlier) detection In a data set, predictive analytics can be used to get a picture of what the data tends to look like in a typical case and to determine if an observation is notably different (or outlying) from this pattern. For example, a sales manager could model the sales results of typical salespeople and use anomaly detection to identify specific salespeople who have unusually high or low sales results. Or the IRS could model typical tax returns and use anomaly detection to identify specific returns that are extremely atypical for review and possible audit.
Association learning This involves identifying items that tend to co-occur and finding the rules that describe their co-occurrence. For example, a supermarket chain once found that men who buy baby diapers on Thursdays also tend to buy beer on Thursdays (possibly in anticipation of watching sports on television over the weekend). This led the chain to display beer near the baby aisle in its stores. As another example, Netflix might find that customers whō rent fictiōnal dramas alsō tênd tō rent historical documentaries ō thăt some customers will rent almost any type of movie that stars a particular actor or actress. Disney might find that visitors who spend more time at the Magic Kingdom also tend to buy Disney cartoon character clothing. Disney might also find that visitors who stay in more luxurious Disney hotels also tend to play golf on Disney courses and take cruises on the Disney Cruise Line. These types of findings are used for targeting coupons, deals, or advertising to the right potential customers.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Ratio, Interval, Ordinal

In Section $1.1$ we said that a variable is quantitative if its possible values are numbers that represent quantities (that is, “how much” or “how many”). In general, a quantitative variable is measured on a scale having a fixed unit of measurement between its possible values. For example, if we measure employees’ salaries to the nearest dollar, then one dollar is the fixed unit of measurement between different employees’ salaries. There are two types of quantitative variables: ratio and interval. A ratio variable is a quantitative variable measured on a scale such that ratios of its values are meaningful and there is an inherently defined zero value. Variables such as salary, height, weight, time, and distance are ratio variables. For example, a distance of zero miles is “no distance at all,” and a town that is 30 miles away is “twice as far” as a town that is 15 miles away.

An interval variable is a quantitative variable where ratios of its values are not meaningful and there is not an inherently defined zero value. Temperature (on the Fahrenheit scale) is an interval variable. For example, zero degrees Fahrenheit does not represent “no heat at all,” just that it is very cold. Thus, there is no inherently defined zero value. Furthermore, ratios of temperatures are not meaningful. For example, it makes no sense to say that $60^{\circ}$ is twice as

warm as $30^{\circ}$. In practice, there are very few interval variables other than temperature. Almost all quantitative variables are ratio variables.

In Section $1.1$ we also said that if we simply record into which of several categories a population (or sample) unit falls, then the variable is qualitative (or eategorical). There are two types of qualitative variables: ordinal and nominative. An ordinal variable is a qualitative variable for which there is a meaningful ordering, or ranking, of the categories. The measurements of an ordinal variable may be nonnumerical or numerical. For example, a student may be asked to rate the teaching effectiveness of a college professor as excellent, good, average, poor, or unsatisfactory. Here, one category is higher than the next one; that is, “excellent” is a higher rating than “good,” “good” is a higher rating than “average,” and so on. Therefore, teaching effectiveness is an ordinal variable having nonnumerical measurements. On the other hand, if (as is often done) we substitute the numbers $4,3,2,1$, and 0 for the ratings excellent through unsatisfactory, then teaching effectiveness is an ordinal variable having numerical measurements.

In practice, hoth numhers and associated words are often presented to respondents asked to rate a person or item. When numbers are used, statisticians debate whether the ordinal variable is “somewhat quantitative.” For example, statisticians who claim that teaching effectiveness rated as $4,3,2,1$, or 0 is not somewhat quantitative argue that the difference between 4 (excellent) and 3 (good) may not be the same as the difference between 3 (good) and 2 (average). Other statisticians argue that as soon as respondents (students) see equally spaced numbers (even though the numbers are described by words), their responses are affected enough to make the variable (teaching effectiveness) somewhat quantitative. Generally speaking, the specific words associated with the numbers probably substantially affect whether an ordinal variable may be considered somewhat quantitative. It is important to note, however, that in practice numerical ordinal ratings are often analyzed as though they are quantitative. Specifically, various arithmetic operations (as discussed in Chapters 2 through 18) are often performed on numerical ordinal ratings. For example, a professor’s teaching effectiveness average and a student’s grade point average are calculated.

To conclude this section, we consider the second type of qualitative variable. A nominative variable is a qualitative variable for which there is no meaningful ordering, or ranking, of the categories. A person’s gender, the color of a car, and an employee’s state of residence are nominative variables.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Stratified Random

It is wise to stratify when the population consists of two or more groups that differ with respect to the variable of interest. For instance, consumers could be divided into strata based on gender, age, ethnic group, or income.

As an example, suppose that a department store chain proposes to open a new store in a location that would serve customers who live in a geographical region that consists of (1) an industrial city, (2) a suburban community, and (3) a rural area. In order to assess the potential profitability of the proposed store, the chain wishes to study the incomes of all households in the region. In addition, the chain wishes to estimate the proportion and the total number of households whose members would be likely to shop at the store. The department store chain feels that the industrial city, the suburban community, and the rural area differ with respect to income and the store’s potential desirability. Therefore, it uses these subpopulations as strata and takes a stratified random sample.

Taking a stratified sample can be advantageous because such a sample takes advantage of the fact that elements in the same stratum are similar to each other. It follows that a stratified sample can provide more accurate information than a random sample of the same size. As a simple example, if all of the elements in each stratum were exactly the same, then examining only one element in each stratum would allow us to describe the entire population. Furthermore, stratification can make a sample easier (or possible) to select. Recall that, in order to take a random sample, we must have a list, or frame of all of the population elements. Although a frame might not exist for the overall population, a frame might exist for each stratum. For example, suppose nearly all the households in the department store’s geographical region have telephones. Although there might not be a telephone directory for the overall geographical region, there might be separate telephone directories for the industrial city, the suburb, and the rural area. For more discussion of stratified random sampling, see Mendenhall, Schaeffer, and Ott (1986).
Sometimes it is advantageous to select a sample in stages. This is a common practice when selecting a sample from a very large geographical region. In such a case, a frame often does not exist. For instance, there is no single list of all registered voters in the United States. There is also no single list of all households in the United States. In this kind of situation, we can use multistage cluster sampling. To illustrate this procedure, suppose we wish to take a sample of registered voters from all registered voters in the United States. We might proceed as follows:
Stage 1: Randomly select a sample of counties from all of the counties in the United States.
Stage 2: Randomly select a sample of townships from each county selected in Stage $1 .$
Stage 3: Randomly select a sample of voting precincts from each township selected in Stage 2.
Stage 4: Randomly select a sample of registered voters from each voting precinct selected in Stage 3 .

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|prescriptive analytics

预测分析是用于在数据集中发现异常、模式和关联的方法，目的是预测未来的结果。预测分析和数据挖掘是有时一起使用的术语，但数据挖掘可能更具体地定义为使用预测分析、计算机科学算法和信息系统技术从大量数据中提取有用的知识。据估计，对于任何数据挖掘项目，大约 65% 到 90% 的时间都花在数据准备上——检查、更正、协调不一致以及以其他方式“清理”数据。此外，虽然预测分析方法在与数据挖掘一起使用时可能对决策者最有用，但正如我们将看到的，在分析较小的数据集时，这些方法也很重要。规范性分析着眼于内部和外部变量和约束，以及从预测分析中获得的预测，以推荐一种或多种行动方案。在本书中，除了直观地使用预测分析的预测来建议业务改进行动方案外，我们不会讨论规范性分析。因此，回到预测分析，我们可以将预测分析的应用大致分为六类：
异常（离群值）检测在数据集中，预测分析可用于了解数据在典型情况下的外观，并确定观察结果是否与该模式显着不同（或异常）。例如，销售经理可以对典型销售人员的销售业绩进行建模，并使用异常检测来识别销售业绩异常高或异常低的特定销售人员。或者，美国国税局可以对典型的纳税申报表进行建模，并使用异常检测来识别非常不典型的特定申报表，以供审查和可能的审计。
关联学习这涉及识别倾向于同时出现的项目并找到描述它们同时出现的规则。例如，一家连锁超市曾经发现，在星期四购买婴儿尿布的男性也倾向于在星期四购买啤酒（可能是为了在周末看电视体育节目）。这导致该连锁店在其商店的婴儿过道附近展示啤酒。再举一个例子，Netflix 可能会发现租用虚构剧集的客户也租用历史纪录片，这样一些客户会租用几乎任何类型的由特定演员或女演员主演的电影。迪士尼可能会发现，在魔法王国逗留时间较长的游客也倾向于购买迪士尼卡通人物服装。迪士尼可能还会发现，入住更豪华的迪士尼酒店的游客也倾向于在迪士尼球场打高尔夫球，并乘坐迪士尼游轮航线。这些类型的调查结果用于将优惠券、交易或广告定位到合适的潜在客户。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Ratio, Interval, Ordinal

在部分1.1我们说，如果一个变量的可能值是代表数量的数字（即“多少”或“多少”），则该变量是定量的。通常，定量变量是在其可能值之间具有固定测量单位的尺度上测量的。例如，如果我们以最接近的美元来衡量员工的工资，那么一美元就是不同员工工资之间的固定计量单位。有两种类型的定量变量：比率和区间。比率变量是按比例测量的定量变量，其值的比率是有意义的，并且存在固有定义的零值。工资、身高、体重、时间和距离等变量是比率变量。例如，零英里的距离是“根本没有距离，

区间变量是一个定量变量，其值的比率没有意义，并且没有固有定义的零值。温度（华氏度）是一个区间变量。例如，零华氏度并不代表“根本没有热量”，只是它非常冷。因此，没有固有定义的零值。此外，温度比没有意义。例如，这样说是没有意义的60∘是两倍

温暖如30∘. 实际上，除了温度之外，几乎没有区间变量。几乎所有的定量变量都是比率变量。

在部分1.1我们还说过，如果我们简单地记录一个总体（或样本）单位属于几个类别中的哪一个，那么变量是定性的（或食的）。有两种类型的定性变量：序数和主格。序数变量是一个定性变量，其中有一个有意义的类别排序或排名。序数变量的测量可以是非数值的或数值的。例如，可能会要求学生将大学教授的教学效果评价为优秀、良好、一般、差或不满意。在这里，一个类别高于下一个类别；也就是说，“优秀”的评分高于“好”，“好”的评分高于“一般”，以此类推。因此，教学效果是一个具有非数值测量的有序变量。另一方面，4,3,2,1, 0 表示优秀到不满意的评分，那么教学效果是一个具有数值测量的序数变量。

在实践中，通常会向被要求对个人或项目进行评分的受访者提供热门数字和相关词。当使用数字时，统计学家会争论序数变量是否“有点定量”。例如，声称教学效果被评为4,3,2,1, 或 0 不是定量的争论 4（优秀）和 3（好）之间的差异可能与 3（好）和 2（平均）之间的差异不同。其他统计学家认为，一旦受访者（学生）看到等距的数字（即使这些数字是用文字描述的），他们的反应就会受到影响，足以使变量（教学效率）在某种程度上量化。一般来说，与数字相关的特定词可能会极大地影响序数变量是否可以被认为是定量的。然而，重要的是要注意，在实践中，数字序数评级通常被分析为好像它们是定量的。具体来说，各种算术运算（如第 2 章到第 18 章所讨论的）通常在数字序数评级上执行。例如，

为了结束本节，我们考虑第二种类型的定性变量。主变量是没有有意义的类别排序或排名的定性变量。一个人的性别、汽车的颜色和员工的居住状态是主变量。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Stratified Random

当总体由两个或多个在感兴趣变量方面不同的组组成时，分层是明智的。例如，可以根据性别、年龄、种族或收入将消费者划分为不同的阶层。

举个例子，假设一家百货连锁店提议在一个地点开设一家新店，该地点将为居住在由 (1) 工业城市、(2) 郊区社区和 (3)一个农村地区。为了评估拟建商店的潜在盈利能力，该连锁店希望研究该地区所有家庭的收入。此外，该连锁店希望估计其成员可能在该商店购物的家庭的比例和总数。百货连锁店认为工业城市、郊区社区和农村地区在收入和商店的潜在吸引力方面存在差异。因此，它使用这些亚群作为分层，并采用分层随机样本。

采用分层样本可能是有利的，因为这样的样本利用了同一层中的元素彼此相似的事实。因此，分层样本可以提供比相同大小的随机样本更准确的信息。举个简单的例子，如果每个层中的所有元素都完全相同，那么只检查每个层中的一个元素就可以让我们描述整个人口。此外，分层可以使样本更容易（或可能）选择。回想一下，为了随机抽取样本，我们必须有一个列表或所有总体元素的框架。尽管可能不存在针对总体人口的框架，但可能存在针对每个阶层的框架。例如，假设百货公司所在地理区域内的几乎所有家庭都有电话。尽管可能没有整个地理区域的电话簿，但工业城市、郊区和农村地区可能有单独的电话簿。有关分层随机抽样的更多讨论，请参见 Mendenhall、Schaeffer 和 Ott (1986)。
有时分阶段选择样本是有利的。这是从非常大的地理区域中选择样本时的常见做法。在这种情况下，框架通常不存在。例如，美国没有所有登记选民的单一名单。美国也没有所有家庭的单一清单。在这种情况下，我们可以使用多阶段整群抽样。为了说明这个过程，假设我们希望从美国所有登记选民中抽取登记选民样本。我们可以如下进行：
阶段 1：从美国所有县中随机选择一个县样本。
第 2 阶段：从阶段选择的每个县随机抽取一个乡镇样本1.
第 3 阶段：从第 2 阶段选择的每个乡镇随机选择一个投票区样本。
第 4 阶段：从第 3 阶段选择的每个投票区随机选择一个登记选民样本。

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|商业分析作业代写Statistical Modelling for Business代考|Probability sampling

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|Probability sampling

Random (or approximately random) sampling-as well as the more advanced kinds of sampling discussed in optional Section 1.7–are types of probability sampling. In general, probability sampling is sampling where we know the chance (or probability) that each element in the population will be included in the sample. If we employ probability sampling, the sample obtained can be used to make valid statistical inferences about the sampled population. However, if we do not employ probability sampling, we cannot make valid statistical inferences.

One type of sampling that is not probability sampling is convenience sampling, where we select elements because they are easy or convenient to sample. For example, if we select people to interview because they look “nice” or “pleasant,” we are using convenience sampling. Another example of convenience sampling is the use of voluntary response samples, which are frequently employed by television and radio stations and newspaper columnists. In such samples, participants self-select-that is, whoever wishes to participate does so (usually expressing some opinion). These samples overrepresent people with strong (usually negative) opinions. For example, the advice columnist Ann Landers once asked her readers, “If you had it to do over again, would you have children?” Of the nearly 10,000 parents who voluntarily responded, 70 percent said that they would not. A probability sample taken a few months later found that 91 percent of parents would have children again.

Another type of sampling that is not probability sampling is judgment sampling, where a person who is extremely knowledgeable about the population under consideration selects population elements that he or she feels are most representative of the population. Because the quality of the sample depends upon the judgment of the person selecting the sample, it is dangerous to use the sample to make statistical inferences about the population.

To conclude this section, we consider a classic example where two types of sampling errors doomed a sample’s ability to make valid statistical inferences. This example occurred prior to the presidential election of 1936 , when the Literary Digest predicted that Alf Landon would defeat Franklin D. Roosevelt by a margin of 57 percent to 43 percent. Instead, Roosevelt won the election in a landslide. Literary Digest’s first error was to send out sample ballots (actually, 10 million ballots) to people who were mainly selected from the Digest’s subscription list and from telephone directories. In 1936 the country had not yet recovered from the Great Depression, and many unemployed and low-income people did not have phones or subscribe to the Digest. The Digest’s sampling procedure excluded these people, who overwhelmingly voted for Roosevelt. Second, only $2.3$ million ballots were returned, resulting in the sample being a voluntary response survey. At the same time, George Gallup, founder of the Gallup Poll, was beginning to establish his survey business. He used a probability sample to correctly predict Roosevelt’s victory. In optional Section $1.8$ we discuss various issues related to designing surveys and more about the errors that can occur in survey samples.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Ethical guidelines for statistical practice

The American Statistical Association, the leading U.S. professional statistical association, has developed the report “Ethical Guidelines for Statistical Practice.” 11 This report provides information that helps statistical practitioners to consistently use ethical statistical practices

and that helps users of statistical information avoid being misled by unethical statistical practices. Unethical statistical practices can take a variety of forms, including:

Improper sampling Purposely selecting a biased sample_for example, using a nonrandom sampling procedure that overrepresents population elements supporting a desired conclusion or that underrepresents population elements not supporting the desired conclusion-is unethical. In addition, discarding already sampled population elements that do not support the desired conclusion is unethical. More will be said about proper and improper sampling later in this chapter.
Misleading charts, graphs, and descriptive measures In Section 2.7, we will present an example of how misleading charts and graphs can distort the perception of changes in salaries over time. Using misleading charts or graphs to make the salary changes seem much larger or much smaller than they really are is unethical. In Section 3.1, we will present an example illustrating that many populations of individual or household incomes contain a small percentage of very high incomes. These very high incomes make the population mean income substantially larger than the population median income. In this situation we will see that the population median income is a better measure of the typical income in the population. Using the population mean income to give an inflated perception of the typical income in the population is unethical.
Inappropriate statistical analysis or inappropriate interpretation of statistical results The American Statistical Association report emphasizes that selecting many different samples and running many different tests can eventually (by random chance alone) produce a result that makes a desired conclusion seem to be true, when the conclusion really isn’t true. Therefore, continuing to sample and run tests until a desired conclusion is obtained and not reporting previously obtained results that do not support the desired conclusion is unethical. Furthermore, we should always report our sampling procedure and sample size and give an estimate of the reliability of our statistical results. Estimating this reliability will be discussed in Chapter 7 and beyond.

The above examples are just an introduction to the important topic of unethical statistical practices. The American Statistical Association report contains 67 guidelines organized into eight areas involving general professionalism and ethical responsibilities. These include responsibilities to clients, to research team colleagues, to research subjects, and to other statisticians, as well as responsibilities in publications and testimony and responsibilities of those who employ statistical practitioners.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Business Analytics and Data Mining

Big data, which sometimes needs quick (sometimes almost real-time) analysis for effective business decision making and which may be too massive to be analyzed by traditional statistical methods, has resulted in an extension of traditional statistics called business analytics. In general, business analytics might be defined as the use of traditional and newly developed statistical methods, advances in information systems, and techniques from management science to continuously and iteratively explore and investigate past business performance, with the purpose of gaining insight and improving business planning and operations. There are three broad categories of business analytics: descriptive analytics, predictive analytics, and prescriptive analytics.
Descriptive analytics
In previous examples we have introduced dot plots, time series plots, bar charts, and histograms and illustrated their use in graphically displaying data. These and other traditional graphicalmethods for displaying data are fully discussed in Chapter 2 . These methods, and more recently developed statistical display techniques designed to take advantage of the dramatic advances in data capture, transmission and storage, make up the toolset of descriptive analytics. Descriptive analytics uses the traditional and or newer graphics to present to executives (and sometimes customers) easy-to-understand visual summaries of up-to-the minute information concerning the operational status of a business. In optional Section $2.8$, we will discuss some of the new graphics, which include gauges, bullet graphs, treemaps, and sparklines. We will also see how they are used with each other and more traditional graphics to form analytic dushbourds, which are part of execuive injormaion sysiems. As an example of une of the new graphics-the bullet graph -we again consider the Disney Parks Case.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Probability sampling

随机（或近似随机）抽样——以及可选的第 1.7 节中讨论的更高级的抽样类型——是概率抽样的类型。一般来说，概率抽样是在我们知道总体中每个元素将包含在样本中的机会（或概率）的情况下进行抽样。如果我们采用概率抽样，则获得的样本可用于对抽样总体做出有效的统计推断。但是，如果我们不采用概率抽样，我们就无法做出有效的统计推断。

一种不是概率抽样的抽样是便利抽样，我们选择元素是因为它们容易或方便抽样。例如，如果我们选择面试的人是因为他们看起来“不错”或“令人愉快”，那么我们使用的是便利抽样。便利抽样的另一个例子是自愿响应样本的使用，电视和广播电台以及报纸专栏作家经常使用这些样本。在这样的样本中，参与者是自我选择的——也就是说，谁愿意参与（通常是表达一些意见）。这些样本过多地代表了具有强烈（通常是负面）意见的人。例如，建议专栏作家安兰德斯曾经问她的读者，“如果重来一次，你会生孩子吗？” 在近万名自愿响应的家长中，70% 的人说他们不会。几个月后采集的概率样本发现，91% 的父母会再次生育孩子。

另一种不是概率抽样的抽样是判断抽样，一个对所考虑的人口非常了解的人选择他或她认为最能代表人口的人口元素。因为样本的质量取决于选择样本的人的判断，所以使用样本对总体进行统计推断是危险的。

为了结束本节，我们考虑一个经典示例，其中两种类型的抽样误差注定了样本做出有效统计推断的能力。这个例子发生在 1936 年总统大选之前，当时《文学文摘》预测阿尔夫·兰登将以 57% 对 43% 的优势击败富兰克林·D·罗斯福。相反，罗斯福以压倒性优势赢得了选举。文学文摘的第一个错误是向主要从文摘订阅名单和电话簿中选出的人发送样本选票（实际上是 1000 万张选票）。1936 年，该国尚未从大萧条中恢复过来，许多失业者和低收入者没有电话，也没有订阅《文摘》。《文摘》的抽样程序排除了这些以压倒性多数投票给罗斯福的人。第二，2.3100 万张选票被退回，因此样本是一项自愿响应调查。与此同时，盖洛普民意调查的创始人乔治盖洛普开始建立他的调查业务。他使用概率样本来正确预测罗斯福的胜利。在可选部分1.8我们讨论了与设计调查相关的各种问题，以及更多关于调查样本中可能出现的错误。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Ethical guidelines for statistical practice

美国领先的专业统计协会美国统计协会制定了报告“统计实践的道德准则”。11 本报告提供的信息有助于统计从业人员始终如一地使用符合道德的统计实践

这有助于统计信息的用户避免被不道德的统计做法误导。不道德的统计做法可以采取多种形式，包括：

抽样不当故意选择有偏差的样本（例如，使用非随机抽样程序，该程序过度代表支持预期结论的总体元素或未充分代表不支持预期结论的总体元素）是不道德的。此外，丢弃不支持预期结论的已抽样人口元素是不道德的。本章稍后将详细介绍正确和不正确的抽样。
误导性图表、图表和描述性度量在第 2.7 节中，我们将展示一个示例，说明误导性图表和图表如何扭曲对工资随时间变化的看法。使用误导性图表或图表使工资变化看起来比实际大得多或小得多是不道德的。在第 3.1 节中，我们将展示一个示例，说明许多个人或家庭收入人群中只有一小部分收入非常高。这些非常高的收入使人口平均收入大大高于人口平均收入。在这种情况下，我们将看到人口中位数收入是衡量人口典型收入的更好指标。使用人口平均收入来夸大人口中的典型收入是不道德的。
不恰当的统计分析或对统计结果的不恰当解释美国统计协会的报告强调，选择许多不同的样本并运行许多不同的测试最终会（仅凭随机机会）产生一个结果，使期望的结论看起来是真实的，而结论确实不是真的。因此，继续采样和运行测试直到获得所需的结论，并且不报告先前获得的不支持所需结论的结果是不道德的。此外，我们应该始终报告我们的抽样程序和样本量，并估计我们的统计结果的可靠性。估计这种可靠性将在第 7 章及以后讨论。

上述示例只是对不道德统计实践这一重要主题的介绍。美国统计协会的报告包含 67 条指南，分为涉及一般专业精神和道德责任的八个领域。其中包括对客户、研究团队同事、研究对象和其他统计人员的责任，以及对出版物和证词的责任以及雇用统计从业人员的责任。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Business Analytics and Data Mining

大数据有时需要快速（有时几乎是实时）分析以进行有效的业务决策，并且可能太大而无法通过传统统计方法进行分析，这导致了传统统计的扩展，称为业务分析。一般而言，业务分析可以定义为使用传统和新开发的统计方法、信息系统的进步以及管理科学技术，以不断迭代地探索和调查过去的业务绩效，目的是获得洞察力和改进业务规划和操作。业务分析分为三大类：描述性分析、预测性分析和规范性分析。
描述性分析
在前面的示例中，我们介绍了点图、时间序列图、条形图和直方图，并说明了它们在图形显示数据中的用途。第 2 章全面讨论了这些和其他用于显示数据的传统图形方法。这些方法，以及最近开发的旨在利用数据捕获、传输和存储方面的巨大进步的统计显示技术，构成了描述性分析的工具集。描述性分析使用传统和/或更新的图形向高管（有时是客户）呈现易于理解的关于企业运营状态的最新信息的视觉摘要。在可选部分2.8，我们将讨论一些新的图形，包括仪表、子弹图、树状图和迷你图。我们还将看到它们如何相互使用，以及如何使用更传统的图形来形成分析 dushbourds，这是 execuive injormaion 系统的一部分。作为新图形的一个例子——子弹图——我们再次考虑迪斯尼公园案例。

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|商业分析作业代写Statistical Modelling for Business代考|The Car Mileage Case: Estimating Mileage

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|The Car Mileage Case: Estimating Mileage

统计代写|商业分析作业代写Statistical Modelling for Business代考|Auto Fuel Economy

Part 1: Auto Fuel Economy Personal budgets, national energy security, and the global environment are all affected by our gasoline consumption. Hybrid and electric cars are a vital part of a long-term strategy to reduce our nation’s gasoline consumption. However, until use of these cars is more widespread and affordable, the most effective way to conserve gasoline is to design gasoline powered cars that are more fuel efficient. ${ }^{5}$ In the short term, “that will give you the biggest bang for your buck,” says David Friedman, research director of the Union of Concerned Scientists’ Clean Vehicle Program.”

In this case study we consider a tax credit offered by the federal government to automakers for improving the fuel econonny of gasuline-powered midsize cars. According to The Fuel Economy Guide-2015 Model Year, virtually every gasoline-powered midsize car equipped with an automatic transmission and a six-cylinder engine has an EPA combined city and highway mileage estimate of 26 miles per gallon (mpg) of less. ${ }^{7}$ As a matter of fact, when this book was written, the mileage leader in this category was the Honda Accord, which registered a combined city and highway mileage of $26 \mathrm{mpg}$. While fuel economy has seen improvement in almost all car categories, the EPA has concluded that an additional $5 \mathrm{mpg}$ increase in fuel economy is significant and feasible. ${ }^{8}$ Therefore, suppose that the government has decided to offer the tax credit to any automaker selling a midsize model with an automatic transmission and a six-cylinder engine that achieves an EPA combined city and highway mileage estimate of at least $31 \mathrm{mpg}$.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Sampling a Process

Part 2: Sampling a Process Consider an automaker that has recently introduced a new midsize model with an automatic transmission and a six-cylinder engine and wishes to demonstrate that this new model qualifies for the tax credit. In order to study the population of all cars of this type that will or could potentially be produced, the automaker will choose a sample of 50 of these cars. The manufacturer’s production operation runs 8 -hour shifts, with 100 midsize cars produced on each shift. When the production process has been finetuned and all start-up problems have been identified and corrected, the automaker will select one car at random from each of 50 consecutive production shifts. Once selected, each car is to be subjected to an $\mathrm{EP}^{3} \mathrm{~A}$ test that determines the EPA combined city and highway mileage of the car.

To randomly select a car from a particular production shift, we number the 100 cars produced on the shift from 00 to 99 and use a random number table or a computer software package to obtain a random number between 00 and 99 . For example, starting in the upper left-hand corner of Table 1.4(a) and proceeding down the two leftmost columns, we see that the first three random numbers between 00 and 99 are 33,3 , and 92 . This implies that we would select car 33 from the first production shift, car 3 from the second production shift, car 92 from the third production shift, and so forth. Moreover, because a new group of 100 cars is produced on each production shift, repeated random numbers would not be discarded. For example, if the 15 th and 29 th random numbers are both 7 , we would select the 7 th car from the 15th production shift and the 7th car from the 29th production shift.

统计代写|商业分析作业代写Statistical Modelling for Business代考|The Sample and Inference

Part 3: The Sample and Inference Suppose that when the 50 cars are selected and tested, the sample of 50 EPA combined mileages shown in Table $1.7$ is obtained. A time series plot of the mileages is given in Figure 1.5. Examining this plot, we see that, although the mileages vary over time, they do not seem to vary in any unusual way. For example, the mileages do not tend to either decrease or increase (as did the basic cable rates in Figure 1.3) over time. This intuitively verifies that the midsize car manufacturing process is producing consistent car mileages over time, and thus we can regard the 50 mileages as an approximately random sample that can be used to make statistical inferences about the porpulation of all

possible midsize car mileages. ${ }^{9}$ Therefore, because the 50 mileages vary from a minimum of $29.8 \mathrm{mpg}$ to a maximum of $33.3 \mathrm{mpg}$, we might conclude that most midsize cars produced by the manufacturing process will obtain between $29.8 \mathrm{mpg}$ and $33.3 \mathrm{mpg}$.

We next suppose that in order to offer its tax credit, the federal government has decided to define the “typical” EPA combined city and highway mileage for a car model as the mean of the population of EPA combined mileages that would be obtained by all cars of this type. Therefore, the government will offer its tax credit to any automaker selling a midsize model equipped with an automatic transmission and a six-cylinder engine that achieves a mean EPA combined mileage of at least $31 \mathrm{mpg}$. As we will see in Chapter 3 , the mean of a population of measurements is the average of the population of measurements. More precisely, the population mean is calculated by adding together the population measurements and then dividing the resulting sum by the number of population measurements. Because it is not feasible to test every new midsize car that will or could potentially be produced, we cannot obtain an EPA combined mileage for every car and thus we cannot calculate the population mean mileage. However, we can estimate the population mean mileage by using the sample mean mileage. To calculate the mean of the sample of 50 EPA combined mileages in Table 1.7, we add together the 50 mileages in Table $1.7$ and divide the resulting sum by 50 . The sum of the 50 mileages can be calculated to be
$$
30.8+31.7+\cdots+31.4=1578
$$
and thus the sample mean mileage is $1578 / 50=31.56$. This sample mean mileage says that we estimate that the mean mileage that would be obtained by all of the new midsize cars that will or could potentially be produced this year is $31.56 \mathrm{mpg}$. Unless we are extremely lucky, however, there will be sampling error. That is, the point estimate of $31.56 \mathrm{mpg}$, which is the average of the sample of 50 randomly selected mileages, will probably not exactly equal the population mean, which is the average mileage that would be obtained by all cars. Therefore, although the estimate $31.56$ provides some evidence that the population mean is at least 31 and thus that the automaker should get the tax credit, it does not provide definitive evidence. To obtain more definitive evidence, we employ what is called statistical modeling. We introduce this concept in the next subsection.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Auto Fuel Economy

第 1 部分：汽车燃油经济性个人预算、国家能源安全和全球环境都受到汽油消耗的影响。混合动力和电动汽车是减少我们国家汽油消耗的长期战略的重要组成部分。然而，在这些汽车的使用更加广泛和负担得起之前，节省汽油的最有效方法是设计更省油的汽油动力汽车。5忧思科学家联盟清洁汽车项目的研究主任大卫弗里德曼说，在短期内，“这将为您带来最大的收益”。

在本案例研究中，我们考虑了联邦政府向汽车制造商提供的税收抵免，以提高汽油动力中型汽车的燃油经济性。根据燃油经济性指南 – 2015 年车型年，几乎每辆配备自动变速器和六缸发动机的汽油动力中型汽车的 EPA 综合城市和高速公路里程估计为每加仑 (mpg) 少 26 英里。7事实上，在本书撰写之时，这一类别中的里程领先者是本田雅阁，它的城市和高速公路里程合计为26米pG. 虽然几乎所有汽车类别的燃油经济性都有所改善，但 EPA 得出的结论是，额外的5米pG燃油经济性的提高是显着且可行的。8因此，假设政府已决定向任何销售配备自动变速箱和六缸发动机的中型车型的汽车制造商提供税收抵免，该车型至少达到 EPA 的城市和高速公路综合里程估计值31米pG.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Sampling a Process

第 2 部分：对过程进行抽样考虑一家汽车制造商，该汽车制造商最近推出了一款配备自动变速器和六缸发动机的新中型车型，并希望证明这种新车型有资格获得税收抵免。为了研究将或可能生产的所有此类汽车的数量，汽车制造商将从这些汽车中选择 50 辆作为样本。该制造商的生产运营 8 小时轮班制，每班生产 100 辆中型汽车。当生产过程经过微调，所有启动问题都被识别和纠正后，汽车制造商将从连续50个生产班次中随机选择一辆汽车。一旦被选中，每辆车都将受到和磷3 一种确定汽车的 EPA 结合城市和高速公路里程的测试。

为了从特定的生产班次中随机选择一辆汽车，我们将班次生产的 100 辆汽车从 00 到 99 编号，并使用随机数表或计算机软件包获得 00 到 99 之间的随机数。例如，从表 1.4(a) 的左上角开始，沿着最左边的两列向下，我们看到 00 和 99 之间的前三个随机数是 33,3 和 92 。这意味着我们将从第一个生产班次中选择汽车 33，从第二个生产班次中选择汽车 3，从第三次生产班次中选择汽车 92，依此类推。此外，由于每个生产班次生产一组新的 100 辆汽车，因此不会丢弃重复的随机数。例如，如果第 15 个和第 29 个随机数都是 7 ，

统计代写|商业分析作业代写Statistical Modelling for Business代考|The Sample and Inference

第 3 部分：样本与推论假设在选择和测试 50 辆汽车时，50 辆 EPA 组合里程的样本如表所示1.7获得。图 1.5 给出了里程的时间序列图。检查这个图，我们看到，虽然里程会随着时间而变化，但它们似乎并没有以任何不寻常的方式发生变化。例如，里程不会随着时间的推移而减少或增加（如图 1.3 中的基本电缆费率那样）。这直观地验证了中型汽车制造过程随着时间的推移产生了一致的汽车里程，因此我们可以将 50 里程视为一个近似随机的样本，可用于对所有汽车的数量进行统计推断

可能的中型汽车里程。9因此，因为 50 里程从最低29.8米pG最多为33.3米pG，我们可以得出结论，制造过程中生产的大多数中型汽车将在29.8米pG和33.3米pG.

我们接下来假设，为了提供税收抵免，联邦政府已决定将汽车模型的“典型”EPA 城市和高速公路总里程定义为所有汽车将获得的 EPA 总里程数的平均值这种类型的。因此，政府将为任何销售配备自动变速器和六缸发动机的中型车型的汽车制造商提供税收抵免，该车型的平均 EPA 综合里程至少为31米pG. 正如我们将在第 3 章中看到的，一组测量值的平均值是一组测量值的平均值。更准确地说，总体平均值是通过将总体测量值相加然后将所得总和除以总体测量值的数量来计算的。因为测试每辆将要或可能生产的新中型汽车是不可行的，我们无法获得每辆汽车的 EPA 组合里程，因此我们无法计算总体平均里程。但是，我们可以通过使用样本平均里程来估计总体平均里程。为了计算表 1.7 中 50 个 EPA 组合里程的样本平均值，我们将表中的 50 个里程加在一起1.7并将所得总和除以 50 。50公里的总和可以计算为
30.8+31.7+⋯+31.4=1578
因此样本平均里程为1578/50=31.56. 这个平均里程样本表明，我们估计今年将或可能生产的所有新中型汽车的平均里程数为31.56米pG. 然而，除非我们非常幸运，否则会有抽样误差。也就是说，点估计31.56米pG，这是 50 个随机选择的里程样本的平均值，可能不会完全等于总体平均值，即所有汽车将获得的平均里程。因此，虽然估计31.56提供了一些证据表明人口平均值至少为 31 岁，因此汽车制造商应该获得税收抵免，但它没有提供明确的证据。为了获得更明确的证据，我们采用了所谓的统计建模。我们将在下一小节中介绍这个概念。

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|商业分析作业代写Statistical Modelling for Business代考|Random Sampling, Three Case Studies

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|Random sampling

If the information contained in a sample is to accurately reflect the population under study, the sample should be randomly selected from the population. To intuitively illustrate random sampling, suppose that a small company employs 15 people and wishes to randomly select two of them to attend a convention. To make the random selections, we number the employees from 1 to 15 , and we place in a hat 15 identical slips of paper numbered from 1 to 15 . We thoroughly mix the slips of paper in the hat and, blindfolded, choose one. The number on the chosen slip of paper identifies the first randomly selected employee. Then, still blindfolded, we choose another slip of paper from the hat. The number on the second slip identifies the second randomly selected employee.

Of course, when the population is large, it is not practical to randomly select slips of paper from a hat. For instance, experience has shown that thoroughly mixing slips of paper (or the like) can be difficult. Further, dealing with many identical slips of paper would be cumbersome and time-consuming. For these reasons, statisticians have developed more efficient and accurate methods for selecting a random sample. To discuss these methods we let $n$ denote the number of elements in a sample. We call $n$ the sample size. We now define a random sample of $n$ elements and explain how to select such a sample ${ }^{2}$.

In making random selections from a population, we can sample with or without replacement. If we sample with replacement, we place the element chosen on any particular selection back into the population. Thus, we give this element a chance to be chosen on any succeeding selection. If we sample without replacement, we do not place the element chosen on a particular selection back into the population. Thus, we do not give this element a chance to be chosen on any succeeding selection. It is best to sample without replacement. Intuitively, this is because choosing the sample without replacement guarantees that all of the elements in the sample will be different, and thus we will have the fullest possible look at the population.

We now introduce three case studies that illustrate (1) the need for a random (or approximately random) sample, (2) how to select the needed sample, and (3) the use of the sample in making statistical inferences.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Selecting a Random Sample

Part 2: Selecting a Random Sample The first step in selecting a random sample is to obtain a numbered list of the population elements. This list is called a frame. Then we can use a random number table or computer-generated random numbers to make random selections from the numbered list. Therefore, in order to select a random sample of 100 employees from the population of 2,136 employees on 500 -minute-per-month cell phone plans, the bank will make a numbered list of the 2,136 employees on 500 -minute plans. The bank can then use a random number table, such as Table 1.4(a) on the next page, to select the random sample. To see how this is done, note that any single-digit number in the table has been chosen in such a way that any of the single-digit numbers between 0 and 9 had the same chance of being chosen. For this reason, we say that any single-digit number in the table is a random number between 0 and 9 . Similarly, any two-digit number in the table is a random number between 00 and 99 , any three-digit number in the table is a random number between 000 and 999 , and so forth. Note that the table entries are segmented into groups of five to make the table easier to read. Because the total number of employees on 500 -minute cell phone plans $(2,136)$ is a four-digit number, we arbitrarily select any set of four digits in the table (we have circled these digits). This number, which is 0511 , identifies the first randomly selected employee. Then, moving in any direction from the 0511 (up, down, right, or left-it does not matter which), we select additional sets of four digits. These succeeding sets of digits identify additional randnmly selected emplnyees. Here we arbitrarily move down from 0511 in the table. The first seven sets of four digits we obtain are
$\begin{array}{lllllll}0511 & 7156 & 0285 & 4461 & 3990 & 4919 & 1915\end{array}$
(See Table 1.4(a) – these numbers are enclosed in a rectangle.) Because there are no employees numbered $7156,4461,3990$, or 4919 (remember only 2,136 employees are on 500 -minute plans), we ignore these numbers. This implies that the first three randomly selected employees are those numbered 0511, 0285, and 1915. Continuing this procedure, we can obtain the entire random sample of 100 employees. Notice that, because we are sampling without replacement, we should ignore any set of four digits previously selected from the random number table.

While using a random number table is one way to select a random sample, this approach has a disadvantage that is illustrated by the current situation. Specifically, because most fourdigit random numbers are not between 0001 and 2136 , obtaining 100 different, four-digit random numbers between 0001 and 2136 will require ignoring a large number of random numbers in the random number table, and we will in fact need to use a random number table that is larger than Table 1.4(a). Although larger random number tables are readily available in books of mathematical and statistical tables, a good altemative is to use a computer

software package, which can generate random numbers that are between whatever values we specify. For example, Table $1.4$ (b) gives the Minitab output of 100 different, four-digit random numbers that are between 0001 and 2136 (note that the “leading 0 ‘ $\mathrm{s}$ ” are not included in these four-digit numbers). If used, the random numbers in Table 1.4(b) would identify the 100 employees that form the random sample. For example, the first three randomly selected employees would be employees 705,1990 , and $1007 .$

Finally, note that computer sofware packages sometimes generate the same random number twice and thus are sampling with replacement. Because we wished to randomly select 100 employees without replacement, we had Minitab generate more than 100 (actually, 110) random numbers. We then ignored the repeated random numbers to obtain the 100 different random numbers in Table $1.4$ (b).

统计代写|商业分析作业代写Statistical Modelling for Business代考|Rating a Bottle Design

Part 1: Rating a Bottle Design The design of a package or bottle can have an important effect on a company’s bottom line. In this case a brand group wishes to research consumer reaction to a new bottle design for a popular soft drink. Because it is impossible to show the new bottle design to “all consumers,” the brand group will use the mall intercept method to select a sample of 60 consumers. On a particular Saturday, the brand group will choose a shopping mall and a sampling time so that shoppers at the mall during the sampling time are a representative cross-section of all consumers. Then, shoppers will be intercepted as they walk past a designated location, will be shown the new bottle, and will be asked to rate the bottle image. For each consumer interviewed, a bottle image composite score will be found by adding the consumer’s numerical responses to the five questions shown in Figure 1.4. It follows that the minimum possible bottle image composite score is 5 (resulting from a response of 1 on all five questions) and the maximum possible bottle image composite score is 35 (resulting from a response of 7 on all five questions). Furthermore, experience has shown that the smallest acceptable bottle image composite score for a successful bottle design is 25 .

Part 2: Selecting an Approximately Random Sample Because it is not possible to list and number all of the shoppers who will be at the mall on this Saturday, we cannot select a random sample of these shoppers. However, we can select an approximately random sample of these shoppers. To see one way to do this, note that there are 6 ten-minute intervals during each hour. and thus there are 60 ten-minute intervals during the 10-hour period from 10 A.M. to 8 P.M. – the time when the shopping mall is open. Therefore, one way to select an approximately random sample is to choose a particular location at the mall that most shoppers will walk by and then randomly select – at the beginning of each ten-minute period-one of the first shoppers who walks by the location. Here, although we could randomly select one person from any reasonable number of shoppers who walk by, we will (arbitrarily) randomly select one of the first five shoppers who walk by. For example, starting in the upper left-hand corner of Table 1.4(a) and proceeding down the first column, note that the first three random numbers between 1 and 5 are 3,5 , and 1 . This implies that ( 1 ) at 10 A.M. we would select the 3 rd customer who walks by; (2) at $10: 10$ A.M. we would select the 5 th shopper who walks by; (3) at 10:20 A.M. we would select the 1 st customer who walks by, and so forth. Furthermore, assume that the composite score ratings of the new bottle design that would be given by all shoppers at the mall on the Saturday are representative of the composite score ratings that would be given by all possible consumers. It then follows that the composite score ratings given by the 60 sampled shoppers can be regarded as an approximately random sample that can be used to make statistical inferences about the population of all possible consumer composite score ratings.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Random sampling

如果样本中包含的信息要准确反映所研究的人群，则应从人群中随机抽取样本。为了直观地说明随机抽样，假设一家小公司雇佣了 15 名员工，并希望随机选择其中两人参加会议。为了进行随机选择，我们将员工编号为 1 到 15 ，并将 15 个相同的纸条放入帽子中，编号从 1 到 15 。我们将帽子里的纸条彻底混合，然后蒙上眼睛，选择一张。所选纸条上的数字标识了第一个随机选择的员工。然后，我们仍然蒙着眼睛，从帽子里挑出另一张纸条。第二张单据上的数字标识了第二个随机选择的员工。

当然，当人口众多时，从帽子中随机选择纸条是不现实的。例如，经验表明，彻底混合纸条（或类似物）可能很困难。此外，处理许多相同的纸条将是麻烦且耗时的。由于这些原因，统计学家开发了更有效和更准确的方法来选择随机样本。为了讨论这些方法，我们让n表示样本中元素的数量。我们称之为n样本量。我们现在定义一个随机样本n元素并解释如何选择这样的样本2.

在从总体中进行随机选择时，我们可以在有或没有放回的情况下进行抽样。如果我们进行替换抽样，我们会将在任何特定选择中选择的元素放回总体中。因此，我们给这个元素一个在任何后续选择中被选择的机会。如果我们不放回抽样，我们不会将在特定选择中选择的元素放回总体中。因此，我们不给这个元素一个在任何后续选择中被选择的机会。最好不更换样品。直观地说，这是因为选择没有替换的样本可以保证样本中的所有元素都是不同的，因此我们将尽可能全面地了解总体。

我们现在介绍三个案例研究，说明（1）需要随机（或近似随机）样本，（2）如何选择所需样本，以及（3）使用样本进行统计推断。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Selecting a Random Sample

第 2 部分：选择随机样本选择随机样本的第一步是获取总体元素的编号列表。该列表称为框架。然后我们可以使用随机数表或计算机生成的随机数从编号列表中进行随机选择。因此，为了从每月 500 分钟手机计划的 2,136 名员工中随机抽取 100 名员工，银行将对 500 分钟计划的 2,136 名员工进行编号列表。然后银行可以使用随机数表，例如下一页的表 1.4(a)，来选择随机样本。要了解这是如何完成的，请注意，表中的任何一位数都是这样选择的，即 0 到 9 之间的任何一位数都有相同的机会被选中。为此原因，我们说表中的任何一位数都是 0 到 9 之间的随机数。类似地，表中任意两位数为 00 到 99 之间的随机数，表中任意三位数为 000 到 999 之间的随机数，以此类推。请注意，表格条目被分成五个一组，以使表格更易于阅读。因为 500 分钟手机计划的员工总数(2,136)是一个四位数字，我们在表格中任意选择一组四位数字（我们已经圈出了这些数字）。这个数字是 0511 ，它标识了第一个随机选择的员工。然后，从 0511 向任何方向移动（上、下、右或左——哪个都无所谓），我们选择额外的四位数字组。这些随后的数字组标识了额外的随机选择的员工。这里我们从表中的 0511 任意下移。我们获得的前七组四位数是
0511715602854461399049191915
（见表 1.4(a)——这些数字用一个矩形括起来。）因为没有员工编号7156,4461,3990，或 4919（请记住，只有 2,136 名员工使用 500 分钟计划），我们忽略这些数字。这意味着前三个随机选择的员工是编号为 0511、0285 和 1915 的员工。继续这个过程，我们可以获得 100 名员工的整个随机样本。请注意，因为我们是在没有放回的情况下进行抽样，所以我们应该忽略之前从随机数表中选择的任何四位数字。

虽然使用随机数表是选择随机样本的一种方法，但这种方法有一个缺点，目前的情况已经说明了这一点。具体来说，由于大多数四位随机数不在 0001 和 2136 之间，因此获得 0001 和 2136 之间的 100 个不同的四位随机数将需要忽略随机数表中的大量随机数，而我们实际上需要使用一个大于表 1.4(a) 的随机数表。虽然在数学和统计表的书中很容易找到更大的随机数表，但一个很好的替代方法是使用计算机

软件包，它可以生成介于我们指定的任何值之间的随机数。例如，表1.4(b) 给出 0001 到 2136 之间的 100 个不同的四位随机数的 Minitab 输出（请注意，“前导 0”s” 不包括在这四位数字中）。如果使用，表 1.4(b) 中的随机数将确定构成随机样本的 100 名员工。例如，前三个随机选择的员工将是员工 705,1990 ，并且1007.

最后，请注意，计算机软件包有时会两次生成相同的随机数，因此是带放回抽样。因为我们希望随机选择 100 名员工而不进行替换，所以我们让 Minitab 生成了 100 多个（实际上是 110 个）随机数。然后我们忽略重复的随机数，得到表中的 100 个不同的随机数1.4(b)。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Rating a Bottle Design

第 1 部分：评估瓶子设计包装或瓶子的设计会对公司的底线产生重要影响。在这种情况下，一个品牌集团希望研究消费者对一种流行软饮料的新瓶子设计的反应。由于无法将新瓶设计展示给“所有消费者”，品牌组将采用商场截取法抽取60名消费者作为样本。在特定的周六，品牌组会选择一个购物中心和一个采样时间，以便在采样时间内在该购物中心的购物者是所有消费者的代表性横截面。然后，当购物者经过指定地点时，他们会被拦截，他们会看到新瓶子，并被要求对瓶子图像进行评分。对于每位受访的消费者，通过将消费者对图 1.4 中所示的五个问题的数字回答相加，可以得出瓶子图像综合得分。因此，最低可能的瓶子图像综合得分为 5（由对所有五个问题的回答为 1 产生），而最高可能的瓶子图像综合得分为 35（由对所有五个问题的回答为 7 产生）。此外，经验表明，成功的瓶子设计可接受的最小瓶子图像综合得分是 25 分。

第 2 部分：选择一个近似随机的样本由于无法列出本周六将在购物中心的所有购物者并对其进行编号，因此我们无法从这些购物者中随机选择一个样本。但是，我们可以从这些购物者中选择一个近似随机的样本。要查看执行此操作的一种方法，请注意每小时有 6 个十分钟间隔。因此，在上午 10 点到晚上 8 点的 10 小时内，有 60 个 10 分钟的间隔——购物中心的营业时间。因此，选择近似随机样本的一种方法是在购物中心选择一个大多数购物者会经过的特定位置，然后随机选择——在每十分钟的开始时——首先经过该位置的购物者之一. 这里，虽然我们可以从任何合理数量的路过的购物者中随机选择一个人，但我们将（任意）随机选择前五名路过的购物者中的一个。例如，从表 1.4(a) 的左上角开始，沿着第一列向下，注意 1 和 5 之间的前三个随机数是 3,5 和 1 。这意味着 (1) 在上午 10 点我们将选择第 3 位路过的顾客；(2) 在10:10上午，我们会选择第 5 个经过的购物者；(3) 在上午 10:20，我们将选择第一个经过的顾客，依此类推。此外，假设周六购物中心所有购物者给出的新瓶子设计的综合评分代表所有可能的消费者给出的综合评分。因此，60 个抽样购物者给出的综合评分可以被视为一个近似随机的样本，可用于对所有可能的消费者综合评分的总体进行统计推断。

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|商业分析作业代写Statistical Modelling for Business代考|Existing sources

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|Existing sources

Sometimes we can use data already gathered by public or private sources. The Internet is an obvious place to search for electronic versions of government publications, company reports, and business journals, but there is also a wealth of information available in the reference section of a good library or in county courthouse records.

If a business wishes to find demographic data about regions of the United States, a natural source is the U.S. Census Bureau’s website at http://www.census.gov. Other useful websites for economic and financial data include the Federal Reserve at http://research.stlouisfed.org /fred $2 /$ and the Bureau of Labor Statistics at http://stats.bls.gov/.

However, given the ease with which anyone can post documents, pictures, blogs, and videos on the Internet, not all sites are equally reliable. Some of the sources will be more useful, exhaustive, and error-free than others. Fortunately, search engines prioritize the lists and provide the most relevant and highly used sites first.

Obviously, performing such web searches costs next to nothing and takes relatively little time, but the tradeoff is that we are also limited in terms of the type of information we are able to find. Another option may be to use a private data source. Most companies keep and use employee records and information about their customers, products, processes (inventory, payroll, manufacturing, and accounting), and advertising results. If we have no affiliation with these companies, however, these data may be difficult to obtain.

Another alternative would be to contact a data collection agency, which typically incurs some kind of cost. You can either buy subscriptions or purchase individual company financial reports from agencies like Bloomberg and Dow Jones \& Company. If you need to collect specific information, some companies, such as ACNielsen and Information Resources, Inc., can be hired to collect the information for a fee. Moreover, no matter what existing source you take data from, it is important to assess how reliable the data are by determing how, when, and where the data were collected.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Experimental and observational studies

There are many instances when the data we need are not readily available from a public or private source. In cases like these, we need to collect the data ourselves. Suppose we work for a beverage company and want to assess consumer reactions to a new bottled water. Because the water has not been marketed yet, we may choose to conduct taste tests, focus groups, or some other market research. As another example, when projecting political election results, telephone surveys and exit polls are commonly used to obtain the information needed to predict voting trends. New drugs for fighting disease are tested by collecting data under carefully controlled and monitored experimental conditions. In many marketing, political, and medical situations of these sorts, companies sometimes hire outside consultants or statisticians to help them obtain appropriate data. Regardless of whetherr newly minted data are gathered in-house or by paid outsiders, this type of data collection requires much more time, effort, and expense than are needed when data can be found from public or private sources.

When initiating a study, we first define our variable of interest, or response variable. Other variables, typically called factors, that may be related to the response variable of interest will also be measured. When we are able to set or manipulate the values of these factors, we have an experimental study. For example, a pharmaceutical company might wish to determine the most appropriate daily dose of a cholesterol-lowering drug for patients having cholesterol levels that are too high. The company can perform an experiment in which one

sample of patients receives a placebo; a second sample receives some low dose; a third a higher dose; and so forth. This is an experiment because the company controls the amount of drug each group receives. The optimal daily dose can be determined by analyzing the patients’ responses to the different dosage levels given.

When analysts are unable to control the factors of interest, the study is observational. In studies of diet and cholesterol, patients’ diets are not under the analyst’s control. Patients are often unwilling or unable to follow prescribed diets; doctors might simply ask patients what they eat and then look for associations between the factor diet and the response variable cholesterol level.

Asking people what they eat is an example of performing a survey. In general, people in a survey are asked questions about their behaviors, opinions, beliefs, and other characteristics. For instance, shoppers at a mall might be asked to fill out a short questionnaire which seeks their opinions about a new bottled water. In other observational studies, we might simply observe the behavior of people. For example, we might observe the behavior of shoppers as they look at a store display, or we might observe the interactions between students and teachers.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Transactional data, data warehousing, and big data

With the increased use of online purchasing and with increased competition, businesses have become more aggressive about collecting information concerning customer transactions. Every time a customer makes an online purchase, more information is obtained than just the details of the purchase itself. For example, the web pages searched before making the purchase and the times that the customer spent looking at the different web pages are recorded. Similarly, when a customer makes an in-store purchase, store clerks often ask for the customer’s address, zip code, e-mail address, and telephone number. By studying past customer behavior and pertinent demographic information, businesses hope to accurately predict customer response to different marketing approaches and leverage these predictions into increased revenues and profits. Dramatic advances in data capture, data transmission, and data storage capabilities are enabling organizations to integrate various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval and has as its ideal objective the creation and maintenance of a central repository for all of an organization’s data. The huge capacity of data warehouses has given rise to the term big data, which refers to massive amounts of data, often collected at very fast rates in real time and in different forms and sometimes needing quick preliminary analysis for effective business decision making.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Existing sources

有时我们可以使用已经通过公共或私人来源收集的数据。互联网是搜索政府出版物、公司报告和商业期刊电子版本的明显场所，但在良好图书馆的参考部分或县法院记录中也有大量信息可用。

如果企业希望查找有关美国地区的人口统计数据，自然来源是美国人口普查局的网站 http://www.census.gov。其他有用的经济和金融数据网站包括美联储 http://research.stlouisfed.org/fred2/和劳工统计局 http://stats.bls.gov/。

然而，鉴于任何人都可以轻松地在 Internet 上发布文档、图片、博客和视频，因此并非所有网站都同样可靠。一些资源将比其他资源更有用、更详尽、更无错误。幸运的是，搜索引擎优先考虑列表并首先提供最相关和使用率最高的网站。

显然，执行这样的网络搜索几乎没有成本，花费的时间也相对较少，但代价是我们在能够找到的信息类型方面也受到限制。另一种选择可能是使用私有数据源。大多数公司保留和使用员工记录和有关其客户、产品、流程（库存、工资单、制造和会计）和广告结果的信息。但是，如果我们与这些公司没有关联，则可能难以获得这些数据。

另一种选择是联系数据收集机构，这通常会产生某种成本。您可以从 Bloomberg 和 Dow Jones \& Company 等机构购买订阅或购买个别公司的财务报告。如果您需要收集特定信息，可以聘请一些公司（例如 ACNielsen 和 Information Resources, Inc.）来收取费用来收集信息。此外，无论您从哪个现有来源获取数据，重要的是通过确定收集数据的方式、时间和地点来评估数据的可靠性。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Experimental and observational studies

在许多情况下，我们需要的数据无法从公共或私人来源获得。在这种情况下，我们需要自己收集数据。假设我们为一家饮料公司工作，并希望评估消费者对新瓶装水的反应。因为水还没有上市，我们可能会选择进行口味测试、焦点小组或其他一些市场调查。另一个例子是，在预测政治选举结果时，通常使用电话调查和出口民意调查来获取预测投票趋势所需的信息。通过在仔细控制和监测的实验条件下收集数据来测试抗击疾病的新药。在许多此类营销、政治和医疗情况下，公司有时会聘请外部顾问或统计人员来帮助他们获得适当的数据。无论新生成的数据是在内部收集还是由有偿的外部人员收集，这种类型的数据收集都需要比从公共或私人来源找到数据所需的更多时间、精力和费用。

在开始一项研究时，我们首先定义我们感兴趣的变量或响应变量。也将测量可能与感兴趣的响应变量相关的其他变量，通常称为因子。当我们能够设置或操纵这些因素的值时，我们就有了一项实验研究。例如，一家制药公司可能希望为胆固醇水平过高的患者确定最合适的降胆固醇药物每日剂量。公司可以进行一项实验，其中一个

患者样本接受安慰剂；第二个样品接受一些低剂量；第三个更高的剂量；等等。这是一个实验，因为公司控制着每组接受的药物量。最佳日剂量可以通过分析患者对不同剂量水平的反应来确定。

当分析师无法控制感兴趣的因素时，该研究是观察性的。在饮食和胆固醇的研究中，患者的饮食不受分析师的控制。患者通常不愿意或无法遵循规定的饮食；医生可能会简单地询问患者他们吃什么，然后寻找因素饮食与反应变量胆固醇水平之间的关联。

询问人们他们吃什么是进行调查的一个例子。一般来说，调查中的人们会被问到有关他们的行为、观点、信仰和其他特征的问题。例如，商场的购物者可能会被要求填写一份简短的问卷，以征求他们对新瓶装水的看法。在其他观察性研究中，我们可能只是观察人们的行为。例如，我们可能会观察购物者在看商店展示时的行为，或者我们可能会观察学生和老师之间的互动。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Transactional data, data warehousing, and big data

随着在线购买的增加和竞争的加剧，企业在收集有关客户交易的信息方面变得更加积极。每次客户进行在线购买时，都会获得更多信息，而不仅仅是购买本身的详细信息。例如，记录购买前搜索的网页以及客户查看不同网页的时间。同样，当顾客在店内购物时，店员经常会询问顾客的地址、邮政编码、电子邮件地址和电话号码。通过研究过去的客户行为和相关的人口统计信息，企业希望准确预测客户对不同营销方法的反应，并将这些预测用于增加收入和利润。数据捕获、数据传输和数据存储能力的巨大进步使组织能够将各种数据库集成到数据仓库中。数据仓库被定义为集中数据管理和检索的过程，其理想目标是为组织的所有数据创建和维护中央存储库。数据仓库的巨大容量催生了大数据一词，它指的是海量的数据，通常以非常快的速度实时以不同的形式收集，有时需要快速的初步分析以做出有效的业务决策。数据仓库被定义为集中数据管理和检索的过程，其理想目标是为组织的所有数据创建和维护中央存储库。数据仓库的巨大容量催生了大数据一词，它指的是海量的数据，通常以非常快的速度实时以不同的形式收集，有时需要快速的初步分析以做出有效的业务决策。数据仓库被定义为集中数据管理和检索的过程，其理想目标是为组织的所有数据创建和维护中央存储库。数据仓库的巨大容量催生了大数据一词，它指的是海量的数据，通常以非常快的速度实时以不同的形式收集，有时需要快速的初步分析以做出有效的业务决策。

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|商业分析作业代写Statistical Modelling for Business代考|An Introduction to Business

Posted on 2022年4月18日2022年4月18日 by statistics-lab

如果你也在怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法，来分析企业之前的商业表现，从而通过分析结果来对未来的商业战略进行预测和指导。

我们提供的商业分析Statistical Modelling for Business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|商业分析作业代写Statistical Modelling for Business代考|An Introduction to Business

统计代写|商业分析作业代写Statistical Modelling for Business代考|Data sets, elements, and variables

We have said that data are facts and figures from which conclusions can be drawn. Together, the data that are collected for a particular study are referred to as a data set. For example, Table $1.1$ is a data set that gives information about the new homes sold in a Florida luxury home development over a recent three-month period. Potential home buyers could choose either the “Diamond” or the “Ruby” home model design and could have the home built on either a lake lot or a treed lot (with no water access).

In order to understand the data in Table 1.1, note that any data set provides information about some group of individual elements, which may be people, objects, events, or other entities. The information that a data set provides about its elements usually describes one or more characteristics of these elements.

For the data set in Table 1.1, each sold home is an element, and four variables are used to describe the homes. These variables are (1) the home model design, (2) the type of lot on which the home was built, (3) the list (asking) price, and (4) the (actual) selling price. Moreover, each home model design came with “everything included”-specifically, a complete, luxury interior package and a choice (at no price difference) of one of three different architectural exteriors. The builder made the list price of each home solely dependent on the model design. However, the builder gave various price reductions for homes built on treed lots.
The data in Table $1.1$ are real (with some minor changes to protect privacy) and were provided by a business executive – a friend of the authors – who recently received a promotion and needed to move to central Florida. While searching for a new home, the executive and his family visited the luxury home community and decided they wanted to purchase a Diamond model on a treed lot. The list price of this home was $\$ 494,000$, but the developer offered to sell it for an “incentive” price of $\$ 469,000$. Intuitively, the incentive price’s $\$ 25,000$ savings off list price seemed like a good deal. However, the executive resisted making an immediate decision. Instead, he decided to collect data on the selling prices of new homes recently sold in the community and use the data to assess whether the developer might accept a lower offer. In order to collect “relevant data,” the executive talked to local real estate professionals and learned that new homes sold in the community during the previous three months were $\mathrm{~ a ~ g o u l ~ i m l i z a u r ~ o f ~ c o r r e n ~ h o m e ~ v a l u e ~ L o x i n g ~ r e a l ~ m a l a t e ~ s a l e x ~ r e c t u r i}$ learned that tive of the community’s new homes had sold in the previous three months. The data given in Table $1.1$ are the data that the executive collected about these five homes.
When the business executive examined Table 1.1, he noted that homes on lake lots had sold at their list price, but homes on treed lots had not. Because the executive and his family wished to purchase a Diamond model on a treed lot, the executive also noted that two Diamond models on treed lots had sold in the previous three months. One of these Diamond models had sold for the incentive price of $\$ 469,000$, but the other had sold for a lower price of $\$ 440,000$. Hoping to pay the lower price for his family’s new home, the executive offered $\$ 440,000$ for the Diamond model on the treed lot. Initially, the home builder turned down this offer, but two days later the builder called back and accepted the offer. The executive had used data to buy the new home for $\$ 54,000$ less than the list price and $\$ 29,000$ less than the incentive price!

统计代写|商业分析作业代写Statistical Modelling for Business代考|Quantitative and qualitative variables

For any variable describing an element in a data set, we carry out a measurement to assign a value of the variable to the element. For example, in the real estate example, real estate sales records gave the actual selling price of each home to the nearest dollar. As another example, a credit card company might measure the time it takes for a cardholder’s bill to be paid to the nearest day. Or, as a third example, an automaker might measure the gasoline mileage obtained by a car in city driving to the nearest one-tenth of a mile per gallon by conducting a mileage test on a driving course prescribed by the Environmental Protection Agency (EPA). If the possible values of a variable are numbers that represent quantities (that is, “how much” or “how many”), then the variable is said to be quantitative. For example, (1) the actual selling price of a home, (2) the payment time of a bill, (3) the gasoline mileage of a car, and (4) the 2014 payroll of a Major League Baseball team are all quantitative variables. Considering the last example, Table $1.2$ in the page margin gives the 2014 payroll (in millions of dollars) for each of the 30 Major League Baseball (MLB) teams. Moreover, Figure $1.1$ portrays the team payrolls as a dot plot. In this plot, each team payroll is shown as a dot located on the real number line-for example, the leftmost dot represents the payroll for the Houston Astros. In general, the values of a quantitative variable are numbers on the real line. In contrast, if we simply record into which of several categories an element falls, then the variable is said to be qualitative or categorical. Examples of categorical variables include (1) a person’s gender, (2) whether a person who purchases a product is satisfied with the product, (3) the type of lot on which a home is built, and (4) the color of a car. . Figure $1.2$ illustrates the categories we might use for the qualitative variable “car color.” This figure is a bar chart showing the 10 most popular (worldwide) car colors for 2012 and the percentages of cars having these colors.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Cross-sectional and time series data

Some statistical techniques are used to analyze cross-sectional data, while others are used to analyze time series data. Cross-sectional data are data collected at the same or approximately the same point in time. For example, suppose that a bank wishes to analyze last month’s cell phone bills for its employees. Then, because the cell phone costs given by these bills are for different employees in the same month, the cell phone costs are cross-sectional data. Time series data are data collected over different time periods. For example, Table $1.3$ presents the average basic cable television rate in the United States for each of the years 1999 to 2009 . Figure $1.3$ is a time series plot-also called a runs plot-of these data. Here we plot each cable rate on the vertical scale versus its corresponding time index (year) on the horizontal scale. For instance, the first cable rate $(\$ 28.92$ ) is plotted versus 1999 , the second cable rate $(\$ 30.37)$ is plotted versus 2000 , and so forth. Examining the time series plot, we see that the cable rates increased substantially from 1999 to 2009 . Finally. because the five homes in Tablc $1.1$ wcre sold over a thrcc-month period that representcd a rclatively stable real estate market, we can consider the data in Table $1.1$ to essentially be cross-sectional data.Primary data are data collected by an individual or business directly through planned experimentation or observation. Secondary data are data taken from an existing source.

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Data sets, elements, and variables

我们已经说过，数据是可以从中得出结论的事实和数据。为特定研究收集的数据一起被称为数据集。例如，表1.1是一个数据集，提供有关最近三个月内佛罗里达州豪宅开发中出售的新房的信息。潜在的购房者可以选择“钻石”或“红宝石”住宅模型设计，并且可以将房屋建在湖边或树木繁茂的地段（没有水路）。

为了理解表 1.1 中的数据，请注意，任何数据集都提供有关某些单独元素组的信息，这些元素可能是人、对象、事件或其他实体。数据集提供的有关其元素的信息通常描述了这些元素的一个或多个特征。

对于表 1.1 中的数据集，每个售出的房屋都是一个元素，四个变量用于描述房屋。这些变量是（1）房屋模型设计，（2）建造房屋的地块类型，（3）标价（要价）和（4）（实际）售价。此外，每个家庭模型设计都带有“包括的一切”——具体来说，一个完整的、豪华的内部包装和三种不同建筑外观之一的选择（没有价格差异）。建筑商使每个房屋的标价完全取决于模型设计。然而，建筑商对建在树丛上的房屋进行了各种降价。
表中数据1.1是真实的（为了保护隐私而做了一些小的改动），并且是由一位商业主管（作者的朋友）提供的，他最近获得了晋升，需要搬到佛罗里达州中部。在寻找新家时，这位高管和他的家人参观了豪宅社区，并决定在一片树木繁茂的土地上购买钻石模型。这个房子的标价是$494,000，但开发商提出以“激励”价格出售$469,000. 直观地说，激励价格$25,000节省标价似乎很划算。然而，这位高管拒绝立即做出决定。相反，他决定收集社区最近出售的新房售价数据，并使用这些数据来评估开发商是否会接受更低的报价。为了收集“相关数据”，这位高管与当地房地产专业人士进行了交谈，了解到过去三个月在该社区出售的新房一种 G这在l 一世米l一世和一种在r 这F C这rr和n H这米和在一种l在和大号这X一世nG r和一种l 米一种l一种吨和 s一种l和X r和C吨在r一世据了解，该社区的新房在过去三个月内已售出。表中给出的数据1.1是行政人员收集的有关这五个房屋的数据。
当业务主管检查表 1.1 时，他注意到湖区的房屋以标价出售，但树木繁茂的房屋却没有。由于该高管及其家人希望在树木繁茂的地段购买钻石模型，该高管还指出，在过去三个月内，已售出两颗树木茂密的钻石模型。其中一款 Diamond 模型以优惠价格售出$469,000，但另一个以较低的价格出售$440,000. 这位高管希望为他家的新房支付较低的价格，他提出$440,000对于树木繁茂的钻石模型。最初，房屋建筑商拒绝了这个提议，但两天后，建筑商回电并接受了这个提议。这位高管曾使用数据购买新房$54,000低于标价和$29,000低于激励价！

统计代写|商业分析作业代写Statistical Modelling for Business代考|Quantitative and qualitative variables

对于描述数据集中元素的任何变量，我们进行测量以将变量的值分配给元素。例如，在房地产示例中，房地产销售记录将每个房屋的实际售价精确到美元。作为另一个例子，信用卡公司可能会测量持卡人账单支付到最近一天所需的时间。或者，作为第三个例子，汽车制造商可能会通过在环境保护署 (EPA) 规定的驾驶路线上进行里程测试来测量汽车在城市中行驶所获得的汽油里程，精确到每加仑十分之一英里。 . 如果变量的可能值是表示数量的数字（即“多少”或“多少”），则称该变量是定量的。例如，（1）房屋的实际售价，（2）账单的支付时间，（3）汽车的汽油里程，（4）美国职棒大联盟2014年的工资单都是定量变量。考虑最后一个例子，表1.2页边距中给出了 30 支美国职棒大联盟 (MLB) 球队中每支球队的 2014 年工资单（以百万美元计）。此外，图1.1将团队工资单描绘为点图。在该图中，每支球队的工资单显示为位于实数线上的一个点——例如，最左边的点代表休斯顿太空人队的工资单。通常，定量变量的值是实线上的数字。相反，如果我们简单地记录一个元素属于几个类别中的哪一个，那么这个变量就被称为是定性的或分类的。分类变量的示例包括（1）一个人的性别，（2）购买产品的人是否对产品感到满意，（3）建造房屋的地块类型，以及（4）房屋的颜色车。. 数字1.2说明了我们可能用于定性变量“汽车颜色”的类别。该图是一个条形图，显示了 2012 年最流行的 10 种（全球）汽车颜色以及拥有这些颜色的汽车的百分比。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Cross-sectional and time series data

一些统计技术用于分析横截面数据，而另一些则用于分析时间序列数据。横截面数据是在相同或大致相同的时间点收集的数据。例如，假设一家银行希望为其员工分析上个月的手机账单。那么，由于这些账单给出的手机费用是针对同一个月不同员工的，所以手机费用是横截面数据。时间序列数据是在不同时间段收集的数据。例如，表1.3显示 1999 年至 2009 年每年美国的平均基本有线电视费率。数字1.3是这些数据的时间序列图 – 也称为运行图。在这里，我们在垂直尺度上绘制每个电缆速率与在水平尺度上对应的时间指数（年）。例如，第一电缆费率($28.92) 相对于 1999 年绘制，第二个电缆速率($30.37)相对于 2000 绘制，依此类推。检查时间序列图，我们看到电缆费率从 1999 年到 2009 年大幅增加。最后。因为 Tablc 中的五家1.1wcre 在 thrcc 个月内售出，代表相对稳定的房地产市场，我们可以考虑表中的数据1.1本质上是横截面数据。主要数据是个人或企业直接通过有计划的实验或观察收集的数据。辅助数据是从现有来源获取的数据。

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