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|Constructing Frequency Distributions and Histograms
The procedure in the preceding box is not the only way to construct a histogram. Often, histograms are constructed more informally. For instance, it is not necessary to set the lower boundary of the first (leftmost) class equal to the smallest measurement in the data. As an example, suppose that we wish to form a histogram of the 50 gas mileages given in Table $1.7$ (page 15). Examining the mileages, we see that the smallest mileage is $29.8 \mathrm{mpg}$ and that the largest mileage is $33.3 \mathrm{mpg}$. Therefore, it would be convenient to begin the first (leftmost) class at $29.5 \mathrm{mpg}$ and end the last (rightmost) class at $33.5 \mathrm{mpg}$. Further, it would be reasonable to use classes that are $.5 \mathrm{mpg}$ in length. We would then use 8 classes: $29.5<30$, $30<30.5,30.5<31,31<31.5,31.5<32,32<32.5,32.5<33$, and $33<33.5$. A histogram of the gas mileages employing these classes is shown in Figure $2.9$.
Sometimes it is desirable to let the nature of the problem determine the histogram classes. For example, to construct a histogram describing the ages of the residents in a city, it might be reasonable to use classes having 10-year lengths (that is, under 10 years, $10-19$ years, 20-29 years, $30-39$ years, and so on).
Notice that in our examples we have used classes having equal class lengths. In general, it is best to use equal class lengths whenever the raw data (that is, all the actual measurements) are available. However, sometimes histograms are formed with unequal class lengths-particularly when we are using published data as a source. Economic data and data in the social sciences are often published in the form of frequency distributions having unequal class lengths. Dealing with this kind of data is discussed in Exercises $2.26$ and $2.27$. Also discussed in these exercises is how to deal with open-ended classes. For example, if we are constructing a histogram describing the yearly incomes of U.S. households, an open-ended class could be households earning over $\$ 500,000$ per year.
As an alternative to constructing a frequency distribution and histogram by hand, we can use software packages such as Excel and Minitab. Each of these packages will automatically define histogram classes for the user. However, these automatically defined classes will not necessarily be the same as those that would be obtained using the manual method we have previously described. Furthermore, the packages define classes by using different methods. (Descriptions of how the classes are defined can often be found in help menus.) For example, Figure $2.10$
gives a Minitab frequency histogram of the payment times in Table $2.4$. Here, Minitab has defined 11 classes and has labeled 5 of the classes on the horizontal axis using midpoints $(12,16,20,24,28)$. It is easy to see that the midpoints of the unlabeled classes are 10,14 , $18,22,26$, and 30 . Moreover, the boundaries of the first class are 9 and 11 , the boundaries of the second class are 11 and 13 , and so forth. Minitab counts frequencies as we have previously described. For instance, one payment time is at least 9 and less than 11 , two payment times are at least 11 and less than 13 , seven payment times are at least 13 and less than 15 , and so forth.
Figure $2.11$ gives an Excel frequency distribution and histogram of the bottle design ratings in Table $1.6$ on page 14. Excel labels histogram classes using their upper class boundaries. For example, the first class has an upper class boundary equal to the smallest rating of 20 and contains only this smallest rating. The boundaries of the second class are 20 and 22 , the boundaries of the third class are 22 and 24 , and so forth. The last class corresponds to ratings more than 36. Excel’s method for counting frequencies differs from that of Minitab (and, therefore, also differs from the way we counted frequencies by hand in Example 2.2). Excel assigns a frequency to a particular class by counting the number of measurements that are greater than the lower boundary of the class and less than or equal to the upper boundary of the class. For example, one bottle design rating is greater than 20 and less than or equal to (that is, at most) 22 . Similarly, 15 bottle design ratings are greater than 32 and at most 34 .
In Figure $2.10$ we have used Minitab to automatically form histogram classes. It is also possible to force software packages to form histogram classes that are defined by the user. We explain how to do this in the appendices at the end of this chapter. Because Excel does not always automatically define acceptable classes, the classes in Figure $2.11$ are a modification of Excel’s automatic classes. We also explain this modification in the appendices at the end of this chapter.
统计代写|商业分析作业代写Statistical Modelling for Business代考|Some common distribution shapes
We often graph a frequency distribution in the form of a histogram in order to visualize the shape of the distribution. If we look at the histogram of payment times in Figure 2.10, we see that the right tail of the histogram is longer than the left tail. When a histogram has this general shape, we say that the distribution is skewed to the right. Here the long right tail tells us that a few of the payment times are somewhat longer than the rest. If we look at the histogram of bottle design ratings in Figure 2.11, we see that the left tail of the histogram is much longer than the right tail. When a histogram has this general shape, we say that the distribution is skewed to the left. Here the long tail to the left tells
us that, while most of the bottle design ratings are concentrated above 25 or so, a few of the ratings are lower than the rest. Finally, looking at the histogram of gas mileages in Figure $2.9$, we see that the right and left tails of the histogram appear to be mirror images of each other. When a histogram has this general shape, we say that the distribution is symmetrical. Moreover, the distribution of gas mileages appears to be piled up in the middle or mound shaped.
Mound-shaped, symmetrical distributions as well as distributions that are skewed to the right or left are commonly found in practice. For example, distributions of scores on standardized tests such as the SAT and ACT tend to be mound shaped and symmetrical, whereas distributions of scores on tests in college statistics courses might be skewed to the left-a few students don’t study and get scores much lower than the rest. On the other hand, economic data such as income data are often skewed to the right-a few people have incomes much higher than most others. Many other distribution shapes are possible. For example, some distributions have two or more peaks-we will give an example of this distribution shape later in this section. It is often very useful to know the shape of a distribution. For example, knowing that the distribution of butle design ratings is skewed to the left suggests that a few consumers may have noticed a problem with design that others didn’t see. Further investigation into why these consumers gave the design low ratings might allow the company to improve the design.
统计代写|商业分析作业代写Statistical Modelling for Business代考|Frequency polygons
Another graphical display that can be used to depict a frequency distribution is a frequency polygon. To construct this graphic, we plot a point above each class midpoint at a height equal to the frequency of the class the height can also be the class relative frequency or class percent frequency if su desired. Then we cunnect the points with line segments. As we will demonstrate in the following example, this kind of graphic can be particularly useful when we wish to compare two or more distributions.Table $2.8$ lists (in increasing order) the scores earned on the first exam by the 40 students in a business statistics course taught by one of the authors several semesters ago. Figure $2.12$ gives a percent frequency polygon for these exam scores. Because exam scores are often reported by using 10-point grade ranges (for instance, 80 to 90 percent), we have defined the following classes: $30<40,40<50,50<60,60<70,70<80,80<90$, and
$90<100$. This is an example of letting the situation determine the classes of a frequency distribution, which is common practice when the situation naturally defines classes. The points that form the polygon have been plotted corresponding to the midpoints of the classes $(35,45,55,65,75,85,95)$. Each point is plotted at a height that equals the percentage of exam scores in its class. For instance, because 10 of the 40 scores are at least 90 and less than 100 , the plot point corresponding to the class midpoint 95 is plotted at a height of 25 percent.
Looking at Figure 2.12, we see that there is a concentration of scores in the 85 to 95 range and another concentration of scores around 65 . In addition, the distribution of scores is somewhat skewed to the left-a few students had scores (in the 30 s and 40 s) that were quite a bit lower than the rest.
This is an example of a distribution having two peaks. When a distribution has multiple peaks, finding the reason for the different peaks often provides useful information. The reason for the two-peaked distribution of exam scores was that some students were not attending class regularly. Students who received scores in the 60 s and below admitted that they were cutting class, whereas students who received higher scores were attending class on a regular basis.
After identifying the reason for the concentrution of lower scores, the instructor established an attendance policy that forced students to attend every class-any student who missed a class was to be dropped from the course. Table $2.9$ presents the scores on the second exam-after the new attendance policy. Figure $2.13$ presents (and allows us to compare) the percent frequency polygons for both exams. We see that the polygon for the second exam is single peaked – the attendance policy ${ }^{4}$ eliminated the concentration of scores in the $60 \mathrm{~s}$, although the scores are still somewhat skewed to the left.
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|Constructing Frequency Distributions and Histograms
前面框中的过程并不是构造直方图的唯一方法。通常，直方图的构造更为非正式。例如，没有必要将第一个（最左边）类的下边界设置为数据中的最小测量值。举个例子，假设我们希望形成表中给出的 50 次油耗的直方图1.7（第 15 页）。检查里程，我们看到最小的里程是29.8米pG并且最大的里程是33.3米pG. 因此，在开始第一节（最左边的）课时会很方便29.5米pG并在最后一个（最右边的）类结束33.5米pG. 此外，使用以下类是合理的.5米pG长度。然后我们将使用 8 个类：29.5<30,30<30.5,30.5<31,31<31.5,31.5<32,32<32.5,32.5<33， 和33<33.5. 使用这些类别的汽油里程直方图如图所示2.9.
有时希望让问题的性质决定直方图的类别。例如，要构建一个描述城市居民年龄的直方图，使用具有 10 年长度的类（即 10 年以下，10−19年，20-29 岁，30−39年等等）。
请注意，在我们的示例中，我们使用了具有相等类长度的类。一般来说，只要有原始数据（即所有实际测量值）可用，最好使用相等的类长度。但是，有时直方图的类长度不相等，尤其是当我们使用已发布的数据作为源时。经济数据和社会科学数据通常以具有不等类长度的频率分布的形式发布。练习中讨论了处理此类数据2.26和2.27. 在这些练习中还讨论了如何处理开放式课程。例如，如果我们正在构建一个描述美国家庭年收入的直方图，那么开放式类别可能是家庭收入超过$500,000每年。
作为手动构建频率分布和直方图的替代方法，我们可以使用 Excel 和 Minitab 等软件包。这些包中的每一个都将自动为用户定义直方图类。但是，这些自动定义的类不一定与使用我们之前描述的手动方法获得的类相同。此外，包使用不同的方法定义类。（如何定义类的描述通常可以在帮助菜单中找到。）例如，图2.10
给出表中付款时间的 Minitab 频率直方图2.4. 在这里，Minitab 定义了 11 个类，并使用中点在水平轴上标记了 5 个类(12,16,20,24,28). 很容易看出，未标记类的中点是 10,14 ，18,22,26, 和 30 . 此外，第一类的边界是 9 和 11 ，第二类的边界是 11 和 13 ，以此类推。正如我们之前描述的，Minitab 会计算频率。例如，一个支付时间至少为 9 且小于 11 ，两次支付时间为至少 11 且小于 13 ，七次支付时间为至少 13 且小于 15 ，等等。
数字2.11给出了表格中瓶子设计评级的 Excel 频率分布和直方图1.6在第 14 页。 Excel 使用其上类边界标记直方图类。例如，第一类的上类边界等于最小评分 20，并且仅包含这个最小评分。第二类的边界是 20 和 22 ，第三类的边界是 22 和 24 ，以此类推。最后一类对应的评分超过 36。Excel 计算频率的方法与 Minitab 的不同（因此，也不同于我们在示例 2.2 中手动计算频率的方法）。Excel 通过计算大于类下界且小于或等于类上界的测量次数来为特定类分配频率。例如，一个瓶子设计等级大于 20 且小于或等于（即，最多） 22 。相似地，
如图2.10我们已经使用 Minitab 自动形成直方图类。也可以强制软件包形成用户定义的直方图类。我们在本章末尾的附录中解释了如何做到这一点。因为 Excel 并不总是自动定义可接受的类，所以图2.11是对 Excel 自动类的修改。我们还在本章末尾的附录中解释了这种修改。
统计代写|商业分析作业代写Statistical Modelling for Business代考|Some common distribution shapes
我们经常以直方图的形式绘制频率分布图，以便可视化分布的形状。如果我们查看图 2.10 中的支付时间直方图，我们会看到直方图的右尾比左尾长。当直方图具有这种一般形状时，我们说分布向右倾斜。在这里，长长的右尾告诉我们，一些支付时间比其他的要长一些。如果我们看一下图 2.11 中瓶子设计评级的直方图，我们会看到直方图的左尾比右尾长得多。当直方图具有这种一般形状时，我们说分布向左倾斜。这里左边的长尾巴告诉
我们认为，虽然大多数瓶子设计评分都集中在 25 左右，但也有少数评分低于其余部分。最后，看图中的油耗直方图2.9，我们看到直方图的左右尾看起来是彼此的镜像。当直方图具有这种一般形状时，我们说分布是对称的。此外，油耗分布呈中间堆积或土丘状。
在实践中通常会发现丘状、对称分布以及向右或向左倾斜的分布。例如，SAT 和 ACT 等标准化考试的分数分布往往呈土丘状且对称，而大学统计学课程的考试分数分布可能偏左——少数学生不学习并获得分数比其他人低得多。另一方面，收入数据等经济数据往往偏右——少数人的收入远高于其他大多数人。许多其他分布形状是可能的。例如，一些分布有两个或多个峰——我们将在本节后面给出一个这种分布形状的例子。了解分布的形状通常非常有用。例如，知道巴特尔设计评级的分布向左倾斜表明一些消费者可能已经注意到其他人没有看到的设计问题。进一步调查为什么这些消费者对设计的评价较低，可能会让公司改进设计。
统计代写|商业分析作业代写Statistical Modelling for Business代考|Frequency polygons
另一种可用于描述频率分布的图形显示是频率多边形。为了构建这个图形，我们在每个类中点上方绘制一个点，高度等于类的频率，如果需要，高度也可以是类相对频率或类百分比频率。然后我们用线段连接点。正如我们将在下面的示例中演示的那样，当我们希望比较两个或多个分布时，这种图形可能特别有用。表2.8列出（按递增顺序）40 名学生在几个学期前作者之一教授的商业统计课程中的第一次考试中获得的分数。数字2.12给出这些考试分数的百分比频率多边形. 由于考试成绩通常使用 10 分等级范围（例如，80% 到 90%）来报告，因此我们定义了以下类别：30<40,40<50,50<60,60<70,70<80,80<90， 和
90<100. 这是让情况确定频率分布的类别的示例，当情况自然定义类别时，这是常见的做法。形成多边形的点对应于类的中点绘制(35,45,55,65,75,85,95). 每个点都绘制在一个高度，该高度等于其班级考试成绩的百分比。例如，因为 40 个分数中有 10 个至少为 90 且小于 100，所以对应于类中点 95 的绘图点绘制在 25% 的高度处。
查看图 2.12，我们看到分数集中在 85 到 95 范围内，另一个分数集中在 65 左右。此外，分数的分布有些偏左——一些学生的分数（在 30 年代和 40 年代）比其他学生低很多。
这是具有两个峰值的分布的示例。当一个分布有多个峰值时，找出不同峰值的原因通常会提供有用的信息。考试成绩出现双峰分布的原因是部分学生没有按时上课。得分在60岁及以下的学生承认他们在逃课，而得分较高的学生则在正常上课。
在确定了分数偏低的原因后，教师制定了一项出勤政策，强制学生每节课都上课——任何缺课的学生都将被拒之门外。桌子2.9在新的考勤政策之后显示第二次考试的分数。数字2.13呈现（并允许我们比较）两种考试的百分比频率多边形。我们看到第二次考试的多边形是单峰的——出勤政策4消除了分数的集中60 s，尽管分数仍然有些偏左。
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。