• 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|The Data Component

A common starting point for a discussion about data is that they are facts. There is a huge philosophical literature on what is a fact. As noted by Mulligan and Correia (2020), a fact is the opposite of theories and values and “are to be distinguished from things, in particular from complex objects, complexes and wholes, and from relations.” Without getting into this philosophy of facts, I will hold that a fact is a checkable or provable entity and, therefore, true. For example, it is true that Washington D.C. is the capital of the United States: it is easily checkable and can be shown to be true. It is also a fact that $1+1=2$. This is checkable by simply counting one finger on your left hand and one finger on your right hand. ${ }^3$

You could have a lot of facts on a topic but they are of little value if they are not

1. organized,
2. subsetted,
3. manipulated, and
4. interpreted
in a meaningfully way to provide insight for a recommendation for an action, the action being the problem solution. Otherwise, the facts are just a collection of valueless things. Their value stems from what you can do with them.

Organizing data, or facts, is a first step in any analytical process and the drive for information. This could involve arranging them in chronological order (e.g., by date and time of a transaction), spatial order (e.g., countries in the Northern and Southern Hemispheres), alphanumeric order, size order, and so on in an infinite number of ways. Transactions data, for example, are facts about units sold of a series of products including what products, who bought them, when they were sold, the amount sold, and prices. They are typically maintained in a file without a discernible order: just product, date, and units. There is no insight or intelligence from this data. In fact, it is somewhat randomly organized based on when orders were placed. ${ }^4$ If sorted by product and date, however, then they are organized and useful, but not much. The best organization is the one most applicable for a practical problem.
In Data Science, statistics, econometrics, machine learning, and other quantitative areas, a common organizational form is a rectangular array consisting of rows and columns: The rows are typically objects and the columns variables or features. An object can be a person (e.g., a customer) or an event (e.g., a transaction). The words object, case, individual, event, observation, and instance are often used interchangeably and I will certainly do this. For the methods considered in this book, each row is an individual case, one case per row and each case is in its own row.

## 商科代写|商业分析作业代写Statistical Modelling for Business代考|The Extractor Component

Finally, you have to apply some methods or procedures to your DataFrame to extract information. Refer back to Fig. $1.2$ for the role and position of an Extractor function in the information chain. This whole book is concerned with these methods. The interpretation of the results to give meaning to the information will be illustrated as I develop and discuss the methods, but the final interpretation is up to you based on your problem, your domain knowledge, and your expertise.

Due to the size and complexity of modern business data sets, the amount and type of information hidden inside them is large, to say the least. There is no one piece of information-no one size fits all-for all business problems. The same data set can be used for multiple problems and can yield multiple types of information. The possibilities are endless. The information content, however, is a function of the size and complexity of the DataFrame you eventually work with. The size is the number of data elements. Since a DataFrame is a rectangular array, the size is #rows $\times$ #columns elements and is given by its shape attribute. Shape is expressed as a tuple written as (rows, columns). For example, it could be $(5,2)$ for a DataFrame with 5 rows and 2 columns and 10 elements. The complexity is the types of data in the DataFrame and is difficult to quantify except to count types. They could be floating point numbers (or, simply, floats), integers (or ints), Booleans, text strings (referred to as objects), datetime values, and more. The larger and more complex the DataFrame, the more information you can extract. Let $I=I$ fformation, $S=$ size and $C=$ complexity. Then Information $=f(S, C)$ with $\partial I / \partial S>0$ and $\partial I / \partial C>0$. For a very large, complex DataFrame, there is a very large amount of information.

The cost of extracting information directly increases with the DataFrame’s size and complexity of the data. If I have 10 sales values, then my data set is small and simple. Minimal information, such as the mean, standard deviation, and range, can be extracted. The cost of extraction is low; just a hand-held calculator is needed. If I have $10 \mathrm{~GB}$ of data, then more can be done but at a greater cost. For data sizes approaching exabytes, the costs are monumental.

There could be an infinite amount of information, even contradictory information, in a large and complex DataFrame. So, when something is extracted, you have to check for its accuracy. For example, suppose you extract information that classifies customers by risk of default on extended credit. This particular classification may not be a good or correct one; that is, the predictive classifier $(P C)$ may not be the best one for determining whether someone is a credit risk or not. Predictive Error Analysis $(P E A)$ is needed to determine how well the $P C$ worked. I discuss this in Chap. 11. In that discussion, I will use a distinction between a training data set and a testing data set for building the classifier and testing it. This means the entire DataFrame will not, and should not, be used for a particular problem. It should be divided into the two parts although that division is not always clear, or even feasible. I will discuss the split into training and testing data sets in Chap.9.
The complexity of the DataFrame is, as I mentioned above, dependent on the types of data. Generally speaking, there are two forms: text and numeric. Other forms such as images and audio are possible but I will restrict myself to these two forms. I have to discuss these data types so that you know the possibilities and their importance and implications. How the two are handled within a DataFrame and with what statistical, econometric, and machine learning tools for the extraction of information is my focus in this book and so I will deal with them in depth in succeeding chapters. I will first discuss text data and then numeric data in the next two subsections.

# 商业分析代写

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

1. 有组织的，
2. 子集，
3. 操纵，和
4. 以有意义的方式进行解释
，以提供对行动建议的洞察力，行动就是问题的解决方案。否则，事实只是一堆毫无价值的东西。它们的价值源于您可以用它们做什么。

## 广义线性模型代考

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。

## 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。