### 统计代写|数据可视化代写Data visualization代考|BISM3204

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代写方面经验极为丰富，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
• Advanced Probability Theory 高等概率论
• 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|数据可视化代写Data visualization代考|Trying to Display Too Much Information

Another common mistake in building effective charts is trying to convey too much information on a single chart, which is a symptom of trying to communicate too many insights to the audience simultaneously.

Consider the case of Keeland Industries, an online company that provides replacement parts for automobiles. It provides both original-equipment manufacturer (OEM) replacement parts and replacement parts made by different manufacturers that are known as aftermarket replacement parts. Because Keeland sells the replacement parts online, it sells parts to customers throughout the United States. For sales tracking and performance measurement purposes, Keeland divides the United States into 12 regions. Keeland’s management team is most interested in comparing the OEM sales across the 12 regions to see which are performing best for OEM sales and in comparing the Aftermarket sales across the 12 regions to see which are performing best for Aftermarket sales.

Figure $3.30$ displays the OEM and Aftermarket sales by region as a clustered column chart. The clustered column chart makes it easy to compare OEM sales to Aftermarket

sales in each region. However, if the potential markets for these different types of parts are different in each region, that comparison is not particularly useful.
Figure $3.31$ displays the OEM and Aftermarket sales by region as a stacked column chart. The stacked column chart makes it much easier to compare the total sales in the different regions. Therefore, a stacked column chart would be a good choice if the goal is to compare total sales (OEM plus Aftermarket) among the 12 regions. However, the stated goal of this visualization is to compare the OEM sales across regions separately from the Aftermarket sales. The best visualization to accomplish the stated goal is to use two separate column charts as shown in Figures $3.32$ and $3.33$.

## 统计代写|数据可视化代写Data visualization代考|Using Excel Default Settings for Charts

Microsoft Excel allows for the creation of a variety of charts and tables to visualize data. However, a common mistake is to use the default output from Excel without considering changes to the design and format of the visualizations it produces. Excel’s default settings are counter to many of the suggestions covered in this chapter (and the rest of this textbook) for creating good data visualizations. Consider Figure $3.34$. This column chart, which was produced using Excel, shows revenues for eight retail store locations in Texas. The company is interested in comparing revenues by location, and specifically in examining the relative performance of the store located in Laredo because this store has recently had a change in management.

Figure $3.34$ suffers from several flaws that prevent it from being an effective data isualization. The data-ink ratio for Figure $3.34$ is low, so we should consider ways of lecluttering the figure. Examining Figure $3.34$ shows that the chart uses ink in several Nays that are not useful in conveying the data. The gridlines used in this chart are not barticularly useful, so they can be removed. We see that Excel automatically titles the chart “Annual Revenue” and uses a legend with “Annual Revenue.” This is redundant nformation, and at least one of these labels should be removed. The following steps can be used to declutter the default chart produced by Excel, increase the data-ink atio, and make the chart more meaningful to the audience.
Step 1. Click anywhere on the chart in the file RetailRevenueChart
Step 2. Click the Chart Elements button $+$
Deselect the check box for Gridlines
Deselect the check box for Legend
Steps 1 and 2 increase the data-ink ratio by decluttering the chart. We can further mprove this chart by adding meaningful ink to the chart and making a few other modfications. For example, the revenue values shown in this chart are from the previous year and are in $1000 \mathrm{~s}$ of dollars. None of this is clear from the chart. To make it easier or the audience to compare the relative amounts of annual revenue by location, we can sort the columns in decreasing order. Finally, because the audience is particularly nterested in annual sales at the Laredo location, we can change the color of the colImn associated with Laredo to draw the audience’s attention to that part of the chart. The following steps create the finished column chart shown in Figure 3.35.

## 统计代写|数据可视化代写Data visualization代考|Too Many Attributes

In Section 3-1, we discuss the importance of using preattentive attributes in data visualizations to make them easy to understand by the audience. However, using too many preattentive attributes in the same visualization can cause confusion for the audience. Consider again the case of Stanley Consulting Group. The company wants to examine how consultant characteristics such as job title, length of time with the company, and highest educational degree attained are related to the amount of billable hours filed by that consultant. Figure $3.36$ attempts to show this information.

All of the information the company wants to consider is shown in Figure 3.36: the number of billable hours for each consultant (on the vertical axis), the length of time at the company (on the horizontal axis), the consultant’s job title (indicated by the color of the marker in the chart), and the highest degree attained by the consultant (indicated by the shape of the marker in the chart). Figure $3.36$ uses several preattentive attributes from Section 3-1 including spatial positioning, shape, and color. However, because we are using many different preattentive attributes, this chart is difficult for an audience to process. It requires the audience to scan back and forth between the markers in the chart, the legends, and the vertical and horizontal axes. Therefore, this is probably not a particularly useful chart.

A better chart than what is shown in Figure $3.36$ would concentrate on examining fewer relationships and using fewer preattentive attributes. The exact choice of which features to show on the chart depends on the goals of the chart and needs of the audience. If it is more important to examine the relationship between billable hours, length of time at the company, and the job title of the consultant, then a chart such as the one shown in Figure $3.37$ is preferred.

## 统计代写|数据可视化代写Data visualization代考|Using Excel Default Settings for Charts

Microsoft Excel 允许创建各种图表和表格来可视化数据。但是，一个常见的错误是使用 Excel 的默认输出，而不考虑对其生成的可视化的设计和格式进行更改。Excel 的默认设置与本章（以及本教科书的其余部分）中关于创建良好数据可视化的许多建议背道而驰。考虑图3.34. 这个使用 Excel 生成的柱形图显示了德克萨斯州八个零售店位置的收入。该公司有兴趣按位置比较收入，特别是检查位于拉雷多的商店的相对业绩，因为这家商店最近发生了管理层变动。

Steps 1 和 2 复选框，通过整理图表来增加数据墨水比率。我们可以通过在图表中添加有意义的墨水并进行一些其他修改来进一步改进此图表。例如，此图表中显示的收入值来自上一年，并且在1000 s美元。从图表上看，这些都不是很清楚。为了让观众更容易按位置比较年收入的相对金额，我们可以按降序对列进行排序。最后，由于观众对拉雷多位置的年销售额特别感兴趣，我们可以更改与拉雷多相关的柱的颜色，以将观众的注意力吸引到图表的那部分。以下步骤创建完成的柱形图，如图 3.35 所示。

## 有限元方法代写

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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。