• 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|商业分析作业代写Statistical Modelling for Business代考|Uncertainty vs. Risk

Uncertainty is a fact of life reflecting our lack of knowledge. It is either spatial (“I don’t know what is happening in Congress today.”) or temporal (“I don’t know what will happen to sales next year.”). In either case, the lack of knowledge is about the state of the world (SOW): what is happening in Congress and what will happen next year. Business textbooks such as Freund and Williams (1969), Spurr and Bonini (1968), and Hildebrand et al. (2005) typically discuss assigning a probability to different $S O W$ s that you could list. The purpose of these probabilities is to enable you to say something about the world before that something materializes. Somehow, and it is never explained how, you assign numeric values representing outcomes, or payoffs, to the $S O W \mathrm{~s}$. The probabilities and associated payoffs are used to calculate an expected or average payoff over all the possible $S O W$ s. Consider, for example, the rate of return on an investment (ROI) in a capital expansion project. The ROI might depend on the average annual growth of real GDP for the next 5 years. Suppose the real GDP growth is simply expressed as declining (i.e., a recession), flat ( $0 \%$ ), slow $(1 \%-2 \%)$, and robust $(>2 \%)$ with assigned probabilities of $0.05,0.20,0.50$, and $0.25$, respectively. These form a probability distribution. Let $p_i$ be the probability state $i$ is realized. Then, $\sum_{i=1}^n p_i=1.0$ for these $n=4$ possible states. I show the $S O W \mathrm{~s}$, probabilities, and $R O I$ values in Table 1.1. The expected $R O I$ is $\sum_{i=1}^4 p_i \times$ $R O I_i=2.15 \%$. This is the amount expected to be earned on average over the next 5 years.

Savage (1972, p. 9) notes that the “world” in the statement “state of the world” is defined for the problem at hand and that you should not take it literally. It is a fluid concept. He states that it is “the object about which the person is concerned.” At the same time, the “state” of the world is a full description of its conditions. Savage (1972) notes that it is “a description of the world, leaving no relevant aspects undescribed.” But he also notes that there is a true state, a “state that does in fact obtain, i.e., the true description of the world.” Unfortunately, it is unknown, and so the best we can do until it is realized or revealed to us is assign probabilities to the occurrence of each state for decision making. These are the probabilities in Table 1.1. More importantly, it is the fact that the true state is unknown, and never will be known until revealed that is the problem. No amount of information will ever completely and perfectly reveal this true state before it occurs.

## 商科代写|商业分析作业代写Statistical Modelling for Business代考|The Data-Information Nexus

To an extent, discussing definitions and terminology is useful for the advancement of scientific and practical solutions for any problem. If you cannot agree on basic terms, then you are doomed at worst and hindered at best from making any progress toward a solution, a decision. You can, however, become so involved in defining terms and so overly concerned about terminology that nothing else maters. Popper too strongly, that
One should never quarrel about words, and never get involved in questions of terminology … What we are really interested in, our real problem,… are problems of theories and their truth.
Popper, a philosopher of science, was concerned about scientific problems. The same sentiment, however, holds for practical problems like the ones you face daily in your business. Despite Popper’s preeminence, you still need some perspective on the foundational units that drive the raison d’etre of BDA: data and information. ${ }^1$ If information is so important for reducing uncertainty, then a logical question to ask is: “What is information?” A subordinate, but equally important, question is:

The words information and data are used as synonyms in everyday conversations. It is not uncommon, for example, to hear a business manager claim in one instance that she has a lot of data and then say in the next instance that she has a lot of information, thus linking the two words to have the same meaning. In fact, the computer systems that manage data are referred to as Information Systems (IS) and the associated technology used in those systems is referred to as Information Technology (IT). ${ }^2$ The C-Level executive in charge of this data and $I T$ infrastructure is the Chief Information Officer $(\mathrm{CIO})$. Notice the repeated use of the word “information.”
Even though people use these two words interchangeably it does not mean they have the same meaning. It is my contention, along with others, that data and information are distinct terms that, yet, have a connection. I will simply state that data are facts, objects that are true on their face, that have to be organized and manipulated to yield insight into something previously unknown. When managed and manipulated, they become information. The organization cannot be without the manipulation and the manipulation cannot be without the organization. The IT group of your business organizes your company’s data but it does not manipulate it to be information. The information is latent, hidden inside the data and must be extracted so it can be used in a decision. I illustrate this connection Fig. 1.2. I will comment on each component in the next few sections.

# 商业分析代写

## 商科代写|商业分析作业代写Statistical Modelling for Business代考|Uncertainty vs. Risk

Savage (1972, p. 9) 指出，“世界状况”陈述中的“世界”是为手头的问题定义的，你不应该从字面上理解它。这是一个流动的概念。他说这是“这个人所关心的对象”。同时，世界的“状态”是对其状况的完整描述。Savage (1972) 指出它是“对世界的描述，没有留下任何未描述的相关方面”。但他也指出存在一种真实的状态，一种“确实获得的状态，即对世界的真实描述”。不幸的是，它是未知的，因此在它被实现或揭示给我们之前我们能做的最好的事情就是为每个状态的发生分配概率以进行决策。这些是表 1.1 中的概率。更重要的是，真实状态不明，在发现问题所在之前永远不会为人所知。在这种真实状态发生之前，再多的信息也无法完全、完美地揭示它。

## 广义线性模型代考

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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。