• 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

A financial model is usually built in order to answer a question or to solve a problem. For example, the question “Should I purchase this new asset?” can result in a model containing cash flow analysis, which compares the cash flow if the asset is purchased versus if it is not purchased. “How much should I pay for this new asset?” is an entirely different question, and the answer will be a single number or a range of possible numbers.
You need to identify the problem before beginning the model-building process.
For example, if the model you’re building is for the purpose of making a decision, you need to build at least two scenarios – one with the existing business and one including the new venture – as well as a comparison between them. Modelers sometimes call this a “do nothing” versus as “do something” scenario. So the model will consist of three components:
3) “Do nothing” scenario

) “Do something” scenario
Scenario comparison
In the example shown in Figure 3 -1, a small bus company has serviced two bus routes for many years. The financial model shows 12 months of historical data and has forecast the next 12 months. Due to a change in demographics and a new train line servicing the area, ticket sales for the northern route have been declining consistently, and the company expects this trend to continue. If the company does nothing, as shown, the profits will more than halve over a two-year period.

You can download a sample copy of this model in File $0302 . x l s x$ at www. dummies . com/go/financialmodelinginexcel fd2e.

You start building this model by creating the three tabs and determining that the comparison sheet should contain a comparison between the two scenarios. Then you design the “do nothing” scenario and then look at how different the numbers are if the company adds a new bus line.

Keeping models consistent is important. For this reason, the “do nothing” scenario contains an extra blank row in each block of data, which is where the new western route can be inserted. The Total Profit line is shown in row 27 of both scenario pages, which makes the model easier to follow, and less prone to error when linking the charts and summary page to the outputs.

## 商科代写|商业建模代写Business Modeling代考|Designing How the Problem’s

When you’ve identified the problem that needs to be solved, it’s very tempting to dive straight in and begin the model-building process, but it’s a good idea to stop for a moment to plan the model and determine how the output will look. When it comes to building a financial model, you want to start with the end in mind.

Start by creating a mockup design of the output page. You can do this in Excel, or by simply sketching it on a whiteboard or paper. It can be difficult to visualize what the output will look like until you have the data in it. Modelers aren’t often the most artistic types, but you should have at least some idea of the elements that need to be on the output page.

For example, for a business case, let’s say you want to show the net present value (NPV), internal rate of return (IRR), and payback period. To do this, you need cash flow, so the key elements will be revenue and expenses, from which you can derive profitability, and then the NPV, IRR, and payback. You can flesh out the outputs page something like the design shown in Figure $3-4$.

A financial model is only as good as its inputs or source data, and a large part of the modeler’s job is often collecting, interpreting, analyzing, and even manipulating or extrapolating the data to go into the model. In many cases, as much time can be spent collecting data as is spent actually building the model, so if you can collect the data in the correct format in the first place, this can save you a lot of time.

You often have to obtain data you need to build the model from other people or external sources, which can be a frustrating and time-consuming process. Here are some guidelines that can make the data-gathering process easier:
s) Let other parties know well in advance what information you need and its purpose.

) Give them a due date that is realistic for them and fits your time frame.
) Design the input sheets in your model so that the data can be pasted directly in.
\\$) Use dummy data in the meantime if you need to so that you can continue building the model while waiting for the information to come in.
) Allow enough time to check the quality and reliability of the information that has been submitted.

3）“什么都不做”场景

) “做点什么”场景

## 商科代写|商业建模代写Business Modeling代考|Designing How the Problem’s

s) 让其他各方提前知道您需要什么信息及其目的。

) 给他们一个对他们来说切合实际并适合您的时间范围的截止日期。) 设计模型中的输入表，以便可以

## 有限元方法代写

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## 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。