### 统计代写|数据可视化代写Data visualization代考|DECO3100

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|Gestalt Principles

Gestalt principles refer to the guiding principles of how people interpret and perceive what they see. These principles can be used in the design of effective data visualizations. The principles generally describe how people define order and meaning in things that they see. We will limit our discussion to the four Gestalt principles that are most closely related to the design of data visualizations: similarity, proximity, enclosure, and connection. An understanding of these principles can help in creating more effective data visualizations and help differentiate between clutter and meaningful design in data visualizations.

The Gestalt principle of similarity states that people consider objects with similar characteristics as belonging to the same group. These characteristics could be color, shape, size, orientation, or any preattentive attribute. When a data visualization includes objects with similar characteristics, it is important to understand that this communicates to the audience that these objects should be seen as belonging to the same group. Figure $3.16$ is a portion of what was shown in Figure 3.10, but here we are using it to represent the Gestalt principle of similarity. The audience will perceive objects that are the same color, or same shape, as belonging to the same group. We need to understand this when we design a visualization and make sure that we only use similar characteristics for objects when they belong to the same group.

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

The Gestalt principle of proximity states that people consider objects that are physically close to one another as belonging to a group. People will generally seek to collect objects that are near each other into a group and separate objects that are far from one another into different groups. The principle of proximity is apparent in many data visualization charts, including scatter charts.

Consider a firm that would like to perform a market segmentation analysis of its customers to learn more about the customers who purchase its products. The company has collected data on the ages and annual incomes of its customers. A simple scatter chart of the age and income of customers is shown in Figure 3.17. Here, our natural inclination is to view this as three distinct groups of customers based on the proximity of the points. This is an example of the Gestalt principle of proximity.

The Gestalt principle of enclosure states that objects that are physically enclosed together are seen as belonging to the same group. We can illustrate this principle using two modified versions of Figure 3.17. First, we can simply reinforce the similarity principle by creating an enclosure of the points that are already in close proximity (see Figure 3.18a). Alternatively, suppose that there is a third attribute of the customers, other than annual income and age, which can be used to group these customers such as educational background. If we want to visually indicate certain customers that share this characteristic of having similar educational backgrounds, then we can use the principle of enclosure to illustrate this even when customers do not appear close together in the chart. This is shown in Figure $3.18 \mathrm{~b}$. Note that the enclosure can be indicated in multiple ways in a chart. In Figure $3.18$ a we have used shaded areas to enclose points. In Figure $3.18 \mathrm{~b}$ we have used dashed boxes. In general, we only need to create a suggestion of enclosure for the audience to view the objects being enclosed as members of the same group.

## 统计代写|数据可视化代写Data visualization代考|Data-Ink Ratio

The concepts of preattentive attributes and Gestalt principles are valuable in understanding features that can be used to visualize data and how visualizations are processed by the mind. However, it is easy to overuse any of the features and diminish the effectiveness of the feature to differentiate and draw attention. A guiding principle for effective data visualizations is that the table or graph should illustrate the data to help the audience generate insights and understanding. The table or graph should not be so cluttered as to disguise the data or be difficult to interpret.

A common way of thinking about this principle is the idea of maximizing the data-ink ratio. The data-ink ratio measures the proportion of “data-ink” to the total amount of ink used in a table or chart, where data-ink is the ink used that is necessary to convey the meaning of the data to the audience. Non-data-ink is ink used in a table or chart that serves no useful purpose in conveying the data to the audience. Note in Figure 3.11a that the pie chart uses color and a legend to differentiate between the eight managers. The bar chart in this figure communicates the same information without either of these features, and so has a higher data-ink ratio.

Let us consider the case of Diaphanous Industries, a firm that produces fine silk clothing products. Diaphanous is interested in tracking the sales of one of its most popular items, a particular style of scarf. Table $3.1$ and Figure $3.20$ provide examples of a table and chart with low data-ink ratios used to display sales of this style of scarf. The data used in this table and figure represent product sales by day. Both of these examples are similar to tables and charts generated with Excel using common default settings. In Table 3.1, most of the gridlines serve no useful purpose. Likewise, in Figure 3.20, the gridlines in the chart add little additional information. In both cases, most of these lines can be deleted without reducing the information conveyed. However, an important piece of intormation is missing from rigure $3.20:$ titles tor axes. Gienerally, axes should always be labeled in a chart. There are rare exceptions to this where both the meaning and unit of measure are obvious such as when the axis displays the names of months (i.e., “January,” “February,” “March,” etc.). For most charts, we recommend labeling the axes to avoid the possibility of misinterpretation by the audience and to reduce the cognitive load required by the audience.

## 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。