### 统计代写|数据可视化作业代写data visualization代考| A Summary Guide to Chart Selection

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化data visualization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。我们的专家在代写数据可视化data visualization方面经验极为丰富，各种代写数据可视化data visualization相关的作业也就用不着说。

• 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|数据可视化作业代写data visualization代考|Guidelines for Selecting a Chart

Recognizing that there are often exceptions to rules and that there are often disagreements even among data visualization experts, we provide general recommendations based on the goal of the visualization and the type of data being analyzed.
Goal: To Show a Relationship

To show a relationship between two quantitative variables, we recommend a scatter chart. An example is the temperature and snowfall data shown in a scatter chart in Figure 2.7. When dealing with three quantitative variables, a bubble chart can be used. Line charts can be used to emphasize the pattern across consecutive data points and are commonly used to display relationships over time. Stock charts show the relationship between time and stock price. Column charts, bar charts, and heat maps can be used to show the relationships that exist between categories.
Goal: To Show Distribution

In addition to being useful for showing the relationships between quantitative variables, scatter and bubble charts can be useful for showing how the quantitative variable values are distributed over the range for each variable. For example, from the scatter chart in Figure $2.7$, we can see that only 2 of the 51 cities have an average annual snowfall greater than 80 inches.
Column and bar charts can be used to show the distribution of a variable of interest over discrete categories or time periods. For example, Figure $2.5$ shows the distribution of zoo attendance by time (month). As previously mentioned, column charts rather than bar charts should be used for distribution over time, as it is more natural represent the progression of time from left to right. A choropleth map shows the distribution of a quantitative or categorical viable over a geographic space. Figures $2.19$ and $2.21$ are examples of these.
Goal: To Show Composition

When the goal is to show the composition of an entity, a good choice is a bar chart, sorted by contribution to the whole. An example is the New York City budget in Figure 2.2. A stacked bar chart is appropriate for showing the composition of different categories and a stacked column chart is good for showing composition over a time series. Figure $2.17$, the sales for Cheetah Sports by region is a good example of a stacked column chart with time series data.
A treemap shows composition in the situation where there is a hierarchical structure among categorical variables. In Figure $2.26$, we see the brand values (the quantitative variable of interest) for companies within industry sectors. For example, the technology sector is composed of six brands in the top ten. All other sectors are composed of only a single brand.
A waterfall chart shows the composition of a quantitative variable of interest over time or category. For example, Figure $2.30$ shows the composition of the final value of gross profit over time. A funnel chart also shows composition in the sense that going from the bottom of the funnel to the top gives the composition of the original set at the top of the funnel. The funnel chart for the hiring process in Figure $2.34$ is an example.

## 统计代写|数据可视化作业代写data visualization代考|Some Charts to Avoid

In this section, we discuss some charts that should be avoided. There are charts that many data visualization experts agree should be avoided. Usually this is because a chart is overly cluttered or takes too much effort for most audiences to interpret the chart quickly and accurately. Here we provide some guidance on charts we believe should be avoided in favor of other types of charts.

As we have already discussed in the data visualization makeover at the beginning of this chapter, many experts suggest that pie charts should be avoided. Instead of a pie chart, consider using a bar chart. This is because science has shown that we are better at assessing differences in length than angle and area. Small differences can be better detected in length than area, especially when sorted by length. Also, using a bar chart simplifies the chart in that there is no longer a need for a different color for each category. Figures $2.1$ and $2.2$ show the difference between the pie chart and the bar chart and illustrate why the latter is preferred.

Another chart to be avoided is a radar chart. A radar chart is a chart that displays multiple quantitative variables on a polar grid with an axis for each variable. The quantitative values on each axis are connected with lines for a given category. Multiple categories can be overlaid on the same radar chart.

Let us consider data on four suppliers of a component needed by Newton Industries. Newton manufactures high-performance desktop computers and has started to vet four possible suppliers of one of the components needed for its computers. Newton’s management needs to select a supplier to provide the component and has collected data on the percentage of late shipments, the percentage of defective components delivered and the cost per unit each supplier would charge. These data are in the file NewtonSuppliers and are shown in Figure 2.35. Figure $2.36$ is the radar chart created from these data.

## 统计代写|数据可视化作业代写data visualization代考|Excel’s Recommended Charts Tool

Excel provides guidance for chart selection through its Recommended Charts tool. The Recommended Charts button is found in the Charts group of the Insert tab on the Ribbon. The following steps demonstrate the use of the Recommended Charts tool using the zoo attendance data in the file Zoo shown in Figure 2.3.

Step 1. Select cells A1:B13 Step 2. Click the Insert tab on the Ribbon Step 3. Click the Recommended Charts button Churs in the Charts group
The Insert Chart dialog box appears as shown in Figure 2.39. Four different chart types are recommended, a column chart (also shown to the right), a bar chart, a funnel chart, and a combination (combo) chart. Clicking on any of the four charts on the left will display that chart enlarged and to the right, in the same way the column chart is displayed on the right in Figure 2.39. This allows you to see an enlarged version of the chart before committing to the chart.
Step 4. Select the Clustered Column chart and click OK
Edit the chart as outlined in Section 2-2.

## 统计代写|数据可视化作业代写data visualization代考|Excel’s Recommended Charts Tool

Excel 通过其推荐图表工具为图表选择提供指导。推荐图表按钮位于功能区插入选项卡的图表组中。以下步骤使用图 2.3 所示文件 Zoo 中的动物园出勤数据演示了推荐图表工具的使用。

## 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。