### 统计代写|商业分析作业代写Statistical Modelling for Business代考|Predictive analytics, data mining

statistics-lab™ 为您的留学生涯保驾护航 在代写商业分析Statistical Modelling for Business方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写商业分析Statistical Modelling for Business方面经验极为丰富，各种代写商业分析Statistical Modelling for Business相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|prescriptive analytics

Predictive analytics are methods used to find anomalies, patterns, and associations in data sets, with the purpose of predicting future outcomes. Predictive analytics and data mining are terms that are sometimes used together, but data mining might more specifically be defined to be the use of predictive analytics, computer science algorithms, and information systems techniques to extract useful knowledge from huge amounts of data. It is estimated that for any data mining project, approximately 65 percent to 90 percent of the time is spent in data preparation – checking, correcting, reconciling inconsistencies in, and otherwise “cleaning” the data. Also, whereas predictive analytics methods might be most useful to decision makers when used with data mining, these methods can also be important, as we will see, when analyzing smaller data sets. Prescriptive analytics looks at internal and extemal variables and constraints, along with the predictions obtained from predictive analytics, to recommend one or more courses of action. In this book, other than intuitively using predictions from predictive analytics to suggest business improvement courses of action, we will not discuss prescriptive analytics. Therefore, returning to predictive analytics, we can roughly classify the applications of predictive analytics into six categories:
Anomaly (outlier) detection In a data set, predictive analytics can be used to get a picture of what the data tends to look like in a typical case and to determine if an observation is notably different (or outlying) from this pattern. For example, a sales manager could model the sales results of typical salespeople and use anomaly detection to identify specific salespeople who have unusually high or low sales results. Or the IRS could model typical tax returns and use anomaly detection to identify specific returns that are extremely atypical for review and possible audit.
Association learning This involves identifying items that tend to co-occur and finding the rules that describe their co-occurrence. For example, a supermarket chain once found that men who buy baby diapers on Thursdays also tend to buy beer on Thursdays (possibly in anticipation of watching sports on television over the weekend). This led the chain to display beer near the baby aisle in its stores. As another example, Netflix might find that customers whō rent fictiōnal dramas alsō tênd tō rent historical documentaries ō thăt some customers will rent almost any type of movie that stars a particular actor or actress. Disney might find that visitors who spend more time at the Magic Kingdom also tend to buy Disney cartoon character clothing. Disney might also find that visitors who stay in more luxurious Disney hotels also tend to play golf on Disney courses and take cruises on the Disney Cruise Line. These types of findings are used for targeting coupons, deals, or advertising to the right potential customers.

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Ratio, Interval, Ordinal

In Section $1.1$ we said that a variable is quantitative if its possible values are numbers that represent quantities (that is, “how much” or “how many”). In general, a quantitative variable is measured on a scale having a fixed unit of measurement between its possible values. For example, if we measure employees’ salaries to the nearest dollar, then one dollar is the fixed unit of measurement between different employees’ salaries. There are two types of quantitative variables: ratio and interval. A ratio variable is a quantitative variable measured on a scale such that ratios of its values are meaningful and there is an inherently defined zero value. Variables such as salary, height, weight, time, and distance are ratio variables. For example, a distance of zero miles is “no distance at all,” and a town that is 30 miles away is “twice as far” as a town that is 15 miles away.

An interval variable is a quantitative variable where ratios of its values are not meaningful and there is not an inherently defined zero value. Temperature (on the Fahrenheit scale) is an interval variable. For example, zero degrees Fahrenheit does not represent “no heat at all,” just that it is very cold. Thus, there is no inherently defined zero value. Furthermore, ratios of temperatures are not meaningful. For example, it makes no sense to say that $60^{\circ}$ is twice as

warm as $30^{\circ}$. In practice, there are very few interval variables other than temperature. Almost all quantitative variables are ratio variables.

In Section $1.1$ we also said that if we simply record into which of several categories a population (or sample) unit falls, then the variable is qualitative (or eategorical). There are two types of qualitative variables: ordinal and nominative. An ordinal variable is a qualitative variable for which there is a meaningful ordering, or ranking, of the categories. The measurements of an ordinal variable may be nonnumerical or numerical. For example, a student may be asked to rate the teaching effectiveness of a college professor as excellent, good, average, poor, or unsatisfactory. Here, one category is higher than the next one; that is, “excellent” is a higher rating than “good,” “good” is a higher rating than “average,” and so on. Therefore, teaching effectiveness is an ordinal variable having nonnumerical measurements. On the other hand, if (as is often done) we substitute the numbers $4,3,2,1$, and 0 for the ratings excellent through unsatisfactory, then teaching effectiveness is an ordinal variable having numerical measurements.

In practice, hoth numhers and associated words are often presented to respondents asked to rate a person or item. When numbers are used, statisticians debate whether the ordinal variable is “somewhat quantitative.” For example, statisticians who claim that teaching effectiveness rated as $4,3,2,1$, or 0 is not somewhat quantitative argue that the difference between 4 (excellent) and 3 (good) may not be the same as the difference between 3 (good) and 2 (average). Other statisticians argue that as soon as respondents (students) see equally spaced numbers (even though the numbers are described by words), their responses are affected enough to make the variable (teaching effectiveness) somewhat quantitative. Generally speaking, the specific words associated with the numbers probably substantially affect whether an ordinal variable may be considered somewhat quantitative. It is important to note, however, that in practice numerical ordinal ratings are often analyzed as though they are quantitative. Specifically, various arithmetic operations (as discussed in Chapters 2 through 18) are often performed on numerical ordinal ratings. For example, a professor’s teaching effectiveness average and a student’s grade point average are calculated.

To conclude this section, we consider the second type of qualitative variable. A nominative variable is a qualitative variable for which there is no meaningful ordering, or ranking, of the categories. A person’s gender, the color of a car, and an employee’s state of residence are nominative variables.

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Stratified Random

It is wise to stratify when the population consists of two or more groups that differ with respect to the variable of interest. For instance, consumers could be divided into strata based on gender, age, ethnic group, or income.

As an example, suppose that a department store chain proposes to open a new store in a location that would serve customers who live in a geographical region that consists of (1) an industrial city, (2) a suburban community, and (3) a rural area. In order to assess the potential profitability of the proposed store, the chain wishes to study the incomes of all households in the region. In addition, the chain wishes to estimate the proportion and the total number of households whose members would be likely to shop at the store. The department store chain feels that the industrial city, the suburban community, and the rural area differ with respect to income and the store’s potential desirability. Therefore, it uses these subpopulations as strata and takes a stratified random sample.

Taking a stratified sample can be advantageous because such a sample takes advantage of the fact that elements in the same stratum are similar to each other. It follows that a stratified sample can provide more accurate information than a random sample of the same size. As a simple example, if all of the elements in each stratum were exactly the same, then examining only one element in each stratum would allow us to describe the entire population. Furthermore, stratification can make a sample easier (or possible) to select. Recall that, in order to take a random sample, we must have a list, or frame of all of the population elements. Although a frame might not exist for the overall population, a frame might exist for each stratum. For example, suppose nearly all the households in the department store’s geographical region have telephones. Although there might not be a telephone directory for the overall geographical region, there might be separate telephone directories for the industrial city, the suburb, and the rural area. For more discussion of stratified random sampling, see Mendenhall, Schaeffer, and Ott (1986).
Sometimes it is advantageous to select a sample in stages. This is a common practice when selecting a sample from a very large geographical region. In such a case, a frame often does not exist. For instance, there is no single list of all registered voters in the United States. There is also no single list of all households in the United States. In this kind of situation, we can use multistage cluster sampling. To illustrate this procedure, suppose we wish to take a sample of registered voters from all registered voters in the United States. We might proceed as follows:
Stage 1: Randomly select a sample of counties from all of the counties in the United States.
Stage 2: Randomly select a sample of townships from each county selected in Stage $1 .$
Stage 3: Randomly select a sample of voting precincts from each township selected in Stage 2.
Stage 4: Randomly select a sample of registered voters from each voting precinct selected in Stage 3 .

## 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。