• 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

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Data sets, elements, and variables

We have said that data are facts and figures from which conclusions can be drawn. Together, the data that are collected for a particular study are referred to as a data set. For example, Table $1.1$ is a data set that gives information about the new homes sold in a Florida luxury home development over a recent three-month period. Potential home buyers could choose either the “Diamond” or the “Ruby” home model design and could have the home built on either a lake lot or a treed lot (with no water access).

In order to understand the data in Table 1.1, note that any data set provides information about some group of individual elements, which may be people, objects, events, or other entities. The information that a data set provides about its elements usually describes one or more characteristics of these elements.

For the data set in Table 1.1, each sold home is an element, and four variables are used to describe the homes. These variables are (1) the home model design, (2) the type of lot on which the home was built, (3) the list (asking) price, and (4) the (actual) selling price. Moreover, each home model design came with “everything included”-specifically, a complete, luxury interior package and a choice (at no price difference) of one of three different architectural exteriors. The builder made the list price of each home solely dependent on the model design. However, the builder gave various price reductions for homes built on treed lots.
The data in Table $1.1$ are real (with some minor changes to protect privacy) and were provided by a business executive – a friend of the authors – who recently received a promotion and needed to move to central Florida. While searching for a new home, the executive and his family visited the luxury home community and decided they wanted to purchase a Diamond model on a treed lot. The list price of this home was $\$ 494,000$, but the developer offered to sell it for an “incentive” price of$\$469,000$. Intuitively, the incentive price’s $\$ 25,000$savings off list price seemed like a good deal. However, the executive resisted making an immediate decision. Instead, he decided to collect data on the selling prices of new homes recently sold in the community and use the data to assess whether the developer might accept a lower offer. In order to collect “relevant data,” the executive talked to local real estate professionals and learned that new homes sold in the community during the previous three months were$\mathrm{~ a ~ g o u l ~ i m l i z a u r ~ o f ~ c o r r e n ~ h o m e ~ v a l u e ~ L o x i n g ~ r e a l ~ m a l a t e ~ s a l e x ~ r e c t u r i}$learned that tive of the community’s new homes had sold in the previous three months. The data given in Table$1.1$are the data that the executive collected about these five homes. When the business executive examined Table 1.1, he noted that homes on lake lots had sold at their list price, but homes on treed lots had not. Because the executive and his family wished to purchase a Diamond model on a treed lot, the executive also noted that two Diamond models on treed lots had sold in the previous three months. One of these Diamond models had sold for the incentive price of$\$469,000$, but the other had sold for a lower price of $\$ 440,000$. Hoping to pay the lower price for his family’s new home, the executive offered$\$440,000$ for the Diamond model on the treed lot. Initially, the home builder turned down this offer, but two days later the builder called back and accepted the offer. The executive had used data to buy the new home for $\$ 54,000$less than the list price and$\$29,000$ less than the incentive price!

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Quantitative and qualitative variables

For any variable describing an element in a data set, we carry out a measurement to assign a value of the variable to the element. For example, in the real estate example, real estate sales records gave the actual selling price of each home to the nearest dollar. As another example, a credit card company might measure the time it takes for a cardholder’s bill to be paid to the nearest day. Or, as a third example, an automaker might measure the gasoline mileage obtained by a car in city driving to the nearest one-tenth of a mile per gallon by conducting a mileage test on a driving course prescribed by the Environmental Protection Agency (EPA). If the possible values of a variable are numbers that represent quantities (that is, “how much” or “how many”), then the variable is said to be quantitative. For example, (1) the actual selling price of a home, (2) the payment time of a bill, (3) the gasoline mileage of a car, and (4) the 2014 payroll of a Major League Baseball team are all quantitative variables. Considering the last example, Table $1.2$ in the page margin gives the 2014 payroll (in millions of dollars) for each of the 30 Major League Baseball (MLB) teams. Moreover, Figure $1.1$ portrays the team payrolls as a dot plot. In this plot, each team payroll is shown as a dot located on the real number line-for example, the leftmost dot represents the payroll for the Houston Astros. In general, the values of a quantitative variable are numbers on the real line. In contrast, if we simply record into which of several categories an element falls, then the variable is said to be qualitative or categorical. Examples of categorical variables include (1) a person’s gender, (2) whether a person who purchases a product is satisfied with the product, (3) the type of lot on which a home is built, and (4) the color of a car. . Figure $1.2$ illustrates the categories we might use for the qualitative variable “car color.” This figure is a bar chart showing the 10 most popular (worldwide) car colors for 2012 and the percentages of cars having these colors.

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Cross-sectional and time series data

Some statistical techniques are used to analyze cross-sectional data, while others are used to analyze time series data. Cross-sectional data are data collected at the same or approximately the same point in time. For example, suppose that a bank wishes to analyze last month’s cell phone bills for its employees. Then, because the cell phone costs given by these bills are for different employees in the same month, the cell phone costs are cross-sectional data. Time series data are data collected over different time periods. For example, Table $1.3$ presents the average basic cable television rate in the United States for each of the years 1999 to 2009 . Figure $1.3$ is a time series plot-also called a runs plot-of these data. Here we plot each cable rate on the vertical scale versus its corresponding time index (year) on the horizontal scale. For instance, the first cable rate $(\$ 28.92$) is plotted versus 1999 , the second cable rate$(\$30.37)$ is plotted versus 2000 , and so forth. Examining the time series plot, we see that the cable rates increased substantially from 1999 to 2009 . Finally. because the five homes in Tablc $1.1$ wcre sold over a thrcc-month period that representcd a rclatively stable real estate market, we can consider the data in Table $1.1$ to essentially be cross-sectional data.Primary data are data collected by an individual or business directly through planned experimentation or observation. Secondary data are data taken from an existing source.

## 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。