商科代写|商业分析作业代写Statistical Modelling for Business代考|DATA603

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
商科代写|商业分析作业代写Statistical Modelling for Business代考|DATA603

商科代写|商业分析作业代写Statistical Modelling for Business代考|Types of Business Problems

What types of business problems warrant $B D A$ ? The types are too numerous to mention, but to give a sense of them consider a few examples:

  • Anomaly Detection: production surveillance, predictive maintenance, manufacturing yield optimization;
  • Fraud detection;
  • Identity theft;
  • Account and transaction anomalies;
  • Customer analytics:
  • Customer Relationship Management (CRM);
  • Churn analysis and prevention;
  • Customer Satisfaction;
  • Marketing cross-sell and up-sell;
  • Pricing: leakage monitoring, promotional effects tracking, competitive price responses;
  • Fulfillment: management and pipeline tracking;
  • Competitive monitoring;
  • Competitive Environment Analysis (CEA); and
  • New Product Development.
    And the list goes on, and on.
    A decision of some type is required for all these problems. New product development best exemplifies a complex decision process. Decisions are made throughout a product development pipeline. This is a series of stages from ideation or conceptualization to product launch and post-launch tracking. Paczkowski (2020) identifies five stages for a pipeline: ideation, design, testing, launch, and post-launch tracking. Decisions are made between each stage whether to proceed to the next one or abort development or even production. Each decision point is marked by a business case analysis that examines the expected revenue and market share for the product. Expected sales, anticipated price points (which are refined as the product moves through the pipeline), production and marketing cost estimates, and competitive analyses that include current products, sales, pricing, and promotions plus competitive responses to the proposed new product, are all needed for each business case assessment. If any of these has a negative implication for the concept, then it will be canceled and removed from the pipeline. Information is needed for each business case check point.

The expected revenue and market share are refined for each business case analysis as new and better information -not data-become available for the items I listed above. More data do become available, of course, as the product is developed, but it is the analysis of that data based on methods described in this book, that provide the information needed to approve or not approve the advancement of the concept to the next stage in the pipeline. The first decision, for example, is simply to begin developing a new product. Someone has to say “Yes” to the question “Should we develop a new product?” The business case analysis provides that decision maker with the information for this initial “Go/No Go” decision. Similar decisions are made at other stages.

Another example is product pricing. This is actually a two-fold decision involving a structure (e.g., uniform pricing or price discrimination to mention two possibilities) and a level within the structure. These decisions are made throughout the product life cycle beginning at the development stage (the launch stage of the pipeline I discussed above) and then throughout the post-launch period until the product is ultimately removed from the market. The wrong price structure and/or level could cost your business lost profit, lost market share, or a lost business. See Paczkowski (2018) for a discussion of the role of pricing and the types of analysis for identifying the best price structure and level. Also see Paczkowski (2020) for new product development pricing at each stage of the pipeline.

商科代写|商业分析作业代写Statistical Modelling for Business代考|The Role of Information in Business Decision Making

Decisions are effective if they solve a problem, such as those I discussed above, and aid rather than hinder your business in succeeding in the market. I will assume your business succeeds if it earns a profit and has a positive return for its owners (shareholders, partners, employees in an employee-owned company) or a sole owner. Information could be about

  • current sales;
  • future sales;
  • the state of the market;
  • consumer, social, and technology trends and developments;
  • customer needs and wants;
  • customer willingness-to-pay;
  • key customer segments;
  • financial developments;
  • supply chain developments; and
  • the size of customer churn.
    This information is input into decisions and like any input, if it is bad, then the decisions will be bad. Basically, the GIGO Principle (Garbage In-Garbage Out) holds. This should be obvious and almost trite. Unfortunately, you do not know when you make your decision if your information is good or bad, or even sufficient. You face uncertainty due to the amount and quality of the information you have available.

Without any information you would just be guessing, and guessing is costly. In Fig. 1.1, I illustrate what happens to the cost of decisions based on the amount of information you have. Without any information, all your decisions are based on pure guesses, hunches, so you are forced to approximate their effect. The approximation could be very naive, based on gut instinct (i.e., an unfounded belief that you know everything) or what happened yesterday or in another business similar to yours (i.e., an analog business).

The cost of these approximations in terms of financial losses, lost market share, or outright bankruptcy can be very high. As the amount of information increases, however, you will have more insight so your approximations (i.e., guesses) improve and the cost of approximations declines. This is exactly what happens during the business case process I described above. More and better information helps the decision makers at each business case stage. The approximations could now be based on trends, statistically significant estimates of impact, or model-based what-if analyses. These are not “data”; they are information.

商科代写|商业分析作业代写Statistical Modelling for Business代考|DATA603


商科代写|商业分析作业代写Statistical Modelling for Business代考|Types of Business Problems


  • 异常检测:生产监控、预测性维护、制造良率优化;
  • 欺诈识别;
  • 身份盗用;
  • 账户及交易异常;
  • 客户分析:
  • 客户关系管理(CRM);
  • 客户流失分析与预防;
  • 顾客满意度;
  • 营销交叉销售和追加销售;
  • 定价:泄漏监控、促销效果跟踪、有竞争力的价格响应;
  • 履行:管理和管道跟踪;
  • 竞争监控;
  • 竞争环境分析(CEA);和
  • 新产品开发。
    所有这些问题都需要某种类型的决定。新产品开发最能说明复杂的决策过程。决策是在整个产品开发流程中做出的。这是从构思或概念化到产品发布和发布后跟踪的一系列阶段。Paczkowski (2020) 确定了管道的五个阶段:构思、设计、测试、发布和发布后跟踪。在每个阶段之间做出决定是继续下一阶段还是中止开发甚至生产。每个决策点都由业务案例分析标记,该分析检查产品的预期收入和市场份额。预期销售额、预期价格点(随着产品在管道中移动而细化)、生产和营销成本估算以及包括当前产品的竞争分析,每个业务案例评估都需要销售、定价和促销以及对拟议新产品的竞争性反应。如果其中任何一个对该概念有负面影响,那么它将被取消并从管道中删除。每个业务案例检查点都需要信息。


另一个例子是产品定价。这实际上是一个双重决策,涉及结构(例如,统一定价或价格歧视两种可能性)和结构内的级别。这些决策是在整个产品生命周期中做出的,从开发阶段(我上面讨论的管道的启动阶段)开始,然后在整个启动后阶段,直到产品最终退出市场。错误的价格结构和/或水平可能会使您的企业损失利润、失去市场份额或失去业务。请参阅 Paczkowski (2018) 讨论定价的作用以及用于确定最佳价格结构和水平的分析类型。另请参阅 Paczkowski (2020) 了解管道每个阶段的新产品开发定价。

商科代写|商业分析作业代写Statistical Modelling for Business代考|The Role of Information in Business Decision Making


  • 当前销售额;
  • 未来的销售;
  • 市场状况;
  • 消费者、社会和技术趋势和发展;
  • 客户的需求和愿望;
  • 客户支付意愿;
  • 关键客户群;
  • 金融发展;
  • 供应链发展;和
  • 客户流失的规模。
    这些信息被输入到决策中,就像任何输入一样,如果它是错误的,那么决策就会是错误的。基本上,GIGO 原则(垃圾进垃圾出)成立。这应该是显而易见的,几乎是陈腐的。不幸的是,您不知道您何时做出决定,您的信息是好是坏,甚至是充分的。由于可用信息的数量和质量,您面临着不确定性。

如果没有任何信息,您将只能猜测,而猜测的代价是昂贵的。在图 1.1 中,我说明了基于您拥有的信息量的决策成本会发生什么变化。在没有任何信息的情况下,你所有的决定都是基于纯粹的猜测和直觉,所以你不得不估计它们的效果。近似值可能非常天真,基于直觉(即毫无根据地相信你知道一切)或昨天发生的事情或在与你的业务类似的另一家公司(即模拟公司)中发生的事情。


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


在概率论概念中,随机过程随机变量的集合。 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。





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。


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


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。


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 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。



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