• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|商业建模代写Business Modeling代考|Recognizing the Dangers of Using Excel

Financial modelers, like anyone working extensively with Excel, are very aware of the inherent risks involved. According to a study by Ray Panko, who is a leading authority on spreadsheet practices, close to 90 percent of spreadsheets contain errors.

Some managers treat models as though they are able to produce the answer to all their business decisions and solve all their business problems. It’s frightening to see the blind faith that many managers have in their financial models.

After reading this book, you should have a good idea of the importance of financial modeling in businesses today. The reliance on Excel-based financial models is so entrenched within the culture of many organizations, and the practice of handing “legacy models” over to junior staff who don’t understand how the models work is a widespread practice. Models that have been used over and over for many years are passed on and reused. As a consultant, I’ve seen this time and again – the user doesn’t understand how the model works, but they’re “fairly confident” it’s giving them the correct results.

Considering the importance of spreadsheets in business, the risk of error is not one to be taken lightly. The European Spreadsheet Risk Group (EuSpRIG) was set up in 1999 purely for the purpose of addressing issues of spreadsheet integrity. They research and report on spreadsheet horror stories, which contain the latest spreadsheet-related errors reported in the media and how they could have been avoided. The disastrous consequences of uncontrolled use of spreadsheets are always disturbing, and make for somewhat gruesome reading.

I’m always slightly terrified when people say that they’re going to go ahead with a multimillion-dollar project “because of the results of the financial model.” It’s very easy to get a formula wrong, or for the input assumptions to be just a few basis points out, all of which may well have a material impact on the output. Tweaking the input assumptions by just a few dollars either way can have a huge impact on cash flow, profitability, and the downright viability of a project!

We know that both formula and logic errors are very easy to make and prevalent in corporate financial models. As a financial modeler, you should be vigilantly looking for errors as you build the model. For strategies for reducing error in your models, turn to Chapter $13 .$

Although the major dangers of using Excel relate to its susceptibility to errors, the related issues of capacity and lack of discipline also warrant a mention. In this section, I take a closer look at each of these issues.

The possibility of error in a model is the number-one thing that keeps a financial modeler awake at night. As a modeler, you must have a healthy respect for spreadsheets and their susceptibility to error.

Imagine you’re working on an exciting new project. You’ve provided a financial model that’s being used for a project or key function of your business. It looks fantastic. People are fired up; money is being spent. But weeks or months into the project, the numbers suddenly aren’t adding up. Why is the project so far over budget? On review, you suddenly realize there has been an error in your original calculations. Yikes! Your credibility and confidence in your work are being questioned, leading to some uncomfortable moments during meetings (not to mention, concern over your future at the company).

What form can these errors take? Generally, modeling errors can be grouped into three broad categories: formula errors, assumptions or input errors, and logic errors.

Formula errors are the easiest errors to make and relatively easy to spot, but they’re horribly embarrassing when they’re discovered. These kinds of “mechanical” errors are also the easiest to avoid by self-checking and correction. Chapter 13 covers some techniques you should employ while building your model to reduce the possibility of formula errors.

## 商科代写|商业建模代写Business Modeling代考|Assumptions or input errors

) 数据输入：如果您要更新运营成本（例如，每周更新一次），则很容易出现数据输入错误。如果这些成本没有正确关联或没有定期更新，您可能会获得不完整或不准确的流程图片。有时将这些信息链接到一个单独的、自动生成的文件，并使用一些新的现代 Excel 工具，例如 Get \& Transform（以前称为 Power Query），可以自动化和加速这个过程。此外，请务必确认谁负责更新电子表格，并确保对流程或更新计划的任何更改不会影响您的模型。
) 用户输入：当您对正在建模的产品或项目不太熟悉时，用户输入错误会更频繁地发生。例如，当涉及到计划的工资成本时，您可能会考虑员工将获得的福利，并假设它将是他们工资的 5%，这是一个相当标准的全面假设。但是，由于您是该组织的新手，您可能无法考虑影响员工福利的其他因素，例如提供公司引以为豪的牙科和医疗计划的成本增加。突然间，这将成本推高到12.5百分之一的工资，把你精心计算的所有员工成本都花光了。

## 有限元方法代写

tatistics-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。

## 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。