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

• 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|Studying Pizza Preferences by Using

Part 1: Studying Pizza Preferences by Using a Frequency Distribution Unfortunately, the raw data in Table $2.1$ do not reveal much useful information about the pattern of pizza preferences. In order to summarize the data in a more useful way, we can construct a frequency distribution. To do this we simply count the number of times each of the six pizza restaurants appears in Table 2.1. We find that Bruno’s appears 8 times, Domino’s appears 2 times, Little Caesars appears 9 times, Papa John’s appears 19 times, Pizza Hut appears 4 times, and Will’s Uptown Pizza appears 8 times. The frequency distribution for the pizza preferences is given in Table $2.2$ on the next page-a list of each of the six restaurants along with their corresponding counts (or frequencies). The frequency distribution shows us how the preferences are distributed among the six restaurants. The purpose of the frequency

distribution is to make the data easier to understand. Certainly, looking at the frequency distribution in Table $2.2$ is more informative than looking at the raw data in Table 2.1. We see that Papa John’s is the most popular restaurant, and that Papa John’s is roughly twice as popular as each of the next three runners-up-Bruno’s, Little Caesars, and Will’s. Finally, Pizza Hut and Domino’s are the least preferred restaurants.

When we wish to summarize the proportion (or fraction) of items in each class, we employ the relative frequency for each class. If the data set consists of $n$ observations, we define the relative frequency of a class as follows:
Relative frequency of a class $=\frac{\text { frequency of the class }}{h}$
This quantity is simply the fraction of items in the class. Further, we can obtain the percent frequency of a class by multiplying the relative frequency by 100 .

Table $2.3$ gives a relative frequency distribution and a percent frequency distribution of the pizza preference data. A relative frequency distribution is a table that lists the relative frequency for each class, and a percent frequency distribution lists the percent frequency for each class. Looking at Table $2.3$, we see that the relative frequency for Bruno’s pizza is $8 / 50=.16$ and that (from the percent frequency distribution) $16 \%$ of the sampled students preferred Bruno’s pizza. Similarly, the relative frequency for Papa John’s pizza is $19 / 50=$ $.38$ and $38 \%$ of the sampled students preferred Papa John’s pizza. Finally, the sum of the relative frequencies in the relative frequency distribution equals $1.0$, and the sum of the percent frequencies in the percent frequency distribution equals $100 \%$. These facts are true for all relative frequency and percent frequency distributions.

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|Studying Pizza Preferences

Part 2: Studying Pizza Preferences by Using Bar Charts and Pie Charts A bar chart is a graphic that depicts a frequency, relative frequency, or percent frequency distribution. For example, Figure $2.1$ gives an Excel bar chart of the pizza preference data. On the horizontal axis we have placed a label for each class (restaurant), while the vertical axis measures frequencies. To construct the bar chart, Excel draws a bar (of fixed width) corresponding to each class label.

Each bar is drawn so that its height equals the frequency corresponding to its label. Because the height of each bar is a frequency, we refer to Figure $2.1$ as a frequency bar chart. Notice that there are gaps between the bars. When data are qualitative, the bars should always be separated by gaps in order to indicate that each class is separate from the others. The bar chart in Figure $2.1$ clearly illustrates that, for example, Papa Juhits pizca is preferred by more sampled students than any other restaurant and Domino’s pizza is least preferred by the sampled students.

If desired, the har heights can represent relative frequencies or percent frequencies For instance, Figure $2.2$ is a Minitab percent bar chart for the pizza preference data. Here the heights of the bars are the percentages given in the percent frequency distribution of Table 2.3. Lastly, the bars in Figures $2.1$ and $2.2$ have been positioned vertically. Because of this, these bar charts are called vertical bar charts. However, sometimes bar charts are constructed with horizontal bars and are called horizontal bar charts.

A pie chart is another graphic that can be used to depict a frequency distribution. When constructing a pie chart, we first draw a circle to represent the entire data set. We then divide the circle into sectors or “pie slices” based on the relative frequencies of the classes. For example, remembering that a circle consists of 360 degrees, Bruno’s Pizza (which has relative frequency .16) is assigned a pie slice that consists of $.16(360)=57.6$ degrees. Similarly, Papa John’s Pizza (with relative frequency .38) is assigned a pie slice having. $.38(360)=$ $136.8$ degrees. The resulting pie chart (constructed using Excel) is shown in Figure $2.3$ on the next page. Here we have labeled the pie slices using the percent frequencies. The pie slices can also be labeled using frequencies or relative frequencies.

## 统计代写|商业分析作业代写Statistical Modelling for Business代考|The Pareto chart (Optional)

Pareto charts are used to help identify important quality problems and opportunities for process improvement. By using these charts we can prioritize problem-solving activities. The Pareto chart is named for Vilfredo Pareto (1848-1923), an Italian economist. Pareto suggested that, in many economies, most of the wealth is held by a small minority of the population. It has been found that the “Pareto principle” often applies to defects. That is, only a few defect types account for most of a product’s quality problems.

To illustrate the use of Pareto charts, suppose that a jelly producer wishes to evaluate the labels being placed on 16-ounce jars of grape jelly. Every day for two weeks, all defective labels found on inspection are classified by type of defect. If a label has more than one defect, the type of defect that is most noticeable is recorded. The Excel output in Figure $2.4$ presents the frequencies and percentages of the types of defects observed over the two-week period.
In general, the first step in setting up a Pareto chart summarizing data concerning types of defects (or categories) is to construct a frequency table like the one in Figure 2.4. Defects or categories should be listed at the left of the table in decreasing order by frequenciesthe defect with the highest frequency will be at the top of the table, the defect with the second-highest frequency below the first, and so forth. If an “other” category is employed, it should be placed at the bottom of the table. The “other” category should not make up 50 percent or more of the total of the frequencies, and the frequency for the “other” category should not exceed the frequency for the defect at the top of the table. If the frequency for the “other” category is too high, data should be collected so that the “other” category can be broken down into new categories. Once the frequency and the percentage for each category are determined, a cumulative percentage for each category is computed. As illustrated in Figure $2.4$, the cumulative percentage for a particular category is the sum of the percentages corresponding to the particular eategory and the categories that are above that category in the table.

## 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。