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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|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
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。